![1
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
S. M. Idrees, M. Nowostawski (eds.), Blockchain Transformations, Signals
and Communication Technology, https://doi.org/10.1007/978-3-031-49593-9_1
Chapter 1
Empowering Education: Leveraging
Blockchain for Secure Credentials
and Lifelong Learning
Adil Marouan, Morad Badrani, Nabil Kannouf, and Abdelaziz Chetouani
1 Introduction
1.1
Background on Blockchain Technology and Its
Applications Beyond Finance
Blockchain technology, initially developed for cryptocurrencies like Bitcoin, has
garnered widespread attention due to its potential to revolutionize various industries
beyond finance. Blockchain is a decentralized and distributed ledger that records
transactions across multiple computers, ensuring transparency, immutability, and
security. While finance was the initial domain where blockchain gained prominence,
its applications have expanded to numerous sectors, including education [16, 22].
Blockchain technology provides several unique features [17] that make it suit-
able for applications beyond finance. One of the key features is decentralization,
which means that no single entity has control over the entire blockchain network.
Instead, the network participants, known as nodes, collectively maintain and vali-
date the transactions and records. This decentralized nature eliminates the need for
intermediaries, reducing costs and increasing efficiency.
Another crucial aspect of blockchain is immutability. Once a transaction or
record is added to the blockchain, it cannot be altered or deleted. This feature
ensures the integrity and trustworthiness of the data stored on the blockchain, mak-
ing it highly resistant to tampering and fraud. Immutability is achieved through
A. Marouan (*) · M. Badrani · A. Chetouani
LaMAO Laboratory, ORAS team, ENCG, Mohammed First University, Oujda, Morocco
e-mail: adil.marouan@ump.ac.ma; m.badrani@ump.ac.ma; a.chetouani@ump.ac.ma
N. Kannouf
LSA laboratory, SOVAI Team, ENSA, Abdelmalek Essaadi University, Alhoceima, Morocco](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-14-320.jpg)
![2
cryptographic techniques and consensus algorithms, ensuring that all network par-
ticipants agree on the validity of transactions [4, 24].
Furthermore, blockchain technology offers enhanced security. Data stored on the
blockchain is encrypted and linked to previous transactions, creating a chain of
blocks that are nearly impossible to manipulate without consensus from the net-
work. Additionally, the decentralized nature of the blockchain reduces the risk of a
single point of failure and makes it more resilient against cyberattacks.
Beyond finance [2], blockchain technology has the potential to transform the
field of education. By leveraging its unique features, blockchain can address various
challenges related to data privacy, security, verification, and accessibility in
education.
1.2
Importance of Exploring Blockchain Technology
in Education
Blockchain technology has emerged as a transformative force across various indus-
tries, and its potential in the field of education is gaining significant attention.
Blockchain, often associated with cryptocurrencies like Bitcoin, is essentially a
decentralized and transparent digital ledger that records and verifies transactions.
However, its application extends far beyond financial systems, offering unique
advantages that can revolutionize the education sector (Fig. 1.1).
This chapter aims to explore the potential of BCT in revolutionizing education.
It addresses key issues related to data privacy, security, verification, and accessibil-
ity within the education system.
Blockchain enhances education
credentialing and verification
with secure storage and reliable
methods like MIT’s Digital
Diploma, reducing fraud and
ensuring trust.
Blockchain-based learning
records facilitate portable and
verified achievements, enabling
seamless credit transfer and
recognition of prior learning
across institutions and industries.
Blockchain ensures secure data
management in education by
leveraging decentralization,
cryptographic algorithms, and
student-controlled selective
sharing, safeguarding student
records and sensitive information
from breaches.
Blockchain’s smart contracts
automate administrative tasks in
education, improving efficiency,
reducing errors, and enabling
personalized student support
through streamlined enrollment,
fee payments, course
registrations, and certification
issuance.
Blockchain enables decentralized
learning platforms, fostering
direct interaction between
students and educators, as
exemplified by platforms like
Teachur and BitDegree.
Fig. 1.1 Applications of BCT in education
A. Marouan et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-15-320.jpg)
![3
2 Blockchain Technology and Education
2.1
Unique Features of Blockchain That Can Address
Educational Challenges
Blockchain technology possesses several unique features that have the potential to
address various challenges faced in the field of education. This section will explore
some of these features and their potential applications in addressing educational
challenges.
(a) Data Integrity and Security: Blockchain’s inherent design ensures data integrity
and security. The immutability of data stored on the blockchain makes it highly
resistant to tampering or unauthorized modifications. This feature can be lever-
aged to address challenges related to student record management, certificate
authentication, and academic credential verification [3], storing educational
records and credentials on the blockchain, educational institutions can maintain
a reliable and tamper-proof repository of student achievements, ensuring the
authenticity and security of educational data [21].
(b) Transparent and Trustworthy System: Blockchain’s transparency and decentral-
ized nature create a trustworthy system for educational transactions. Smart con-
tracts, self-executing agreements built on blockchain, can facilitate transparent
and automated processes in various areas, such as student enrollment, course
registration, and financial transactions. These smart contracts can streamline
administrative processes, reduce fraud, and enhance trust among stake-
holders [2].
(c) Portable and Lifelong Learning Records [14]: Blockchain technology enables
the creation of portable and interoperable learning records. Students can have
ownership and control over their educational achievements, which can be
securely stored on the blockchain. This feature allows for the seamless transfer
of learning records between educational institutions, supporting lifelong learn-
ing and facilitating credential recognition.
(d) Microcredentialing and Personalized Learning: Blockchain can enable the issu-
ance and management of microcredentials, which are digital badges represent-
ing specific skills or competencies. Microcredentials can be verified and shared
securely, allowing individuals to demonstrate their skills beyond traditional
degrees or certifications. This supports personalized learning pathways,
enabling learners to showcase their diverse skills and achievements.
(e) Enhanced Collaboration and Content Sharing: Blockchain technology can
facilitate decentralized and peer-to-peer collaboration among learners and edu-
cators. Blockchain-based platforms can provide secure environments for shar-
ing educational resources, fostering collaboration, and incentivizing
contributions through tokens or rewards. This decentralized approach promotes
the creation and sharing of open educational resources, encouraging innovation
and knowledge exchange.
1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-16-320.jpg)
![4
By leveraging the unique features of blockchain, educational institutions can over-
come challenges related to data security, transparency, portability of records, and
collaboration. However, it is important to carefully consider the implementation of
blockchain in education and address technical, regulatory, and ethical consider-
ations to maximize the potential benefits.
2.2
Potential Benefits of Implementing Blockchain
in Education
Implementing blockchain technology in education holds the potential to bring about
several significant benefits. This section will explore some of the potential advan-
tages that blockchain can offer to the field of education.
Enhanced Data Security and Privacy: Blockchain ensures data security and pri-
vacy by decentralizing and making it tamper-resistant, enhancing the protection of
educational records and sensitive student information [3]. This can help protect
against data breaches and ensure the integrity of academic records.
Improved Verification and Credentialing: Blockchain revolutionizes credential
verification by creating a tamper-proof repository of academic records, enabling
easy and authentic verification for employers and institutions, reducing reliance on
paper-based methods [14]. This streamlined and efficient verification process can
help address issues related to credential fraud and enhance trust in the educa-
tional system.
Increased Transparency and Accountability: Blockchain’s transparency and
auditability promote accountability in education by recording transactions and
ensuring transparent processes, preventing fraud and fostering integrity in institu-
tions [2].Additionally, the decentralized nature of blockchain can foster trust among
stakeholders, as all participants have access to the same verified information.
Facilitated Micropayments and Royalties: Blockchain technology enables the
use of smart contracts, which are self-executing contracts with predefined rules and
conditions. Smart contracts can facilitate micropayments and royalties for educa-
tional content creators, such as authors, instructors, or developers of educational
resources. Through blockchain-based platforms, creators can receive fair compen-
sation for their work, fostering innovation and encouraging the production of high-
quality educational materials [14].
Streamlined Administrative Processes: Blockchain has the potential to stream-
line administrative processes in the education sector. By leveraging smart contracts,
tasks such as student enrollment, course registration, and financial transactions can
be automated and executed with increased efficiency. This can reduce administra-
tive burdens, minimize errors, and free up valuable resources for educational
institutions.
Open and Collaborative Educational Ecosystem: Blockchain technology can
facilitate the creation of an open and collaborative educational ecosystem. It can
A. Marouan et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-17-320.jpg)
![5
enable the sharing and verification of educational resources, fostering collaboration
among educators and learners. Blockchain-based platforms can provide secure
environments for the creation, sharing, and adaptation of open educational resources,
ensuring attribution, and incentivizing contributions [14].
2.3
Examples of Universities and Educational Institutions
Implementing Blockchain
These examples showcase how universities around the world have started to inte-
grate blockchain technology into their educational processes, ranging from issuing
digital credentials to conducting research and offering specialized courses. The
adoption of blockchain in education is still evolving, and more institutions are likely
to explore its potential in the future:
• MIT Media Lab (Massachusetts Institute of Technology): The MIT Media
Lab has developed a blockchain-based platform called “Blockcerts” for issuing
and verifying digital credentials. This platform allows students to store and share
their academic achievements securely using blockchain technology.
• Stanford University: Stanford has conducted research on using blockchain to
secure and streamline academic transcripts. The university explored how block-
chain can enhance data security and reduce administrative burdens related to
transcript management.
• University of Sydney: The University of Sydney has explored blockchain tech-
nology to create a platform for students to store and share their academic creden-
tials securely. This initiative aims to simplify the verification process for both
students and employers.
• King Abdullah University of Science and Technology (KAUST): KAUST in
Saudi Arabia has partnered with IBM to explore the use of blockchain technol-
ogy in various educational and research contexts.
• Mohammed First University: Researchers in Moroccan universities are cur-
rently working on utilizing blockchain technology in electronic voting.
3
Verifiable Digital Credentials
3.1
The Need for Secure and Verifiable Digital Credentials
in Education
In the digital era, the traditional methods of issuing and verifying educational cre-
dentials are facing challenges in terms of security, portability, and efficiency. As a
result, there is a growing need for secure and verifiable digital credentials in the
1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-18-320.jpg)
![6
field of education. Some of the reasons why secure and verifiable digital credentials
are crucial in education and provide supporting references are mitigating credential
fraud. Credential fraud, including the fabrication or alteration of academic achieve-
ments, is a significant concern in the education sector. Traditional paper-based cre-
dentials are susceptible to forgery and tampering, making it difficult to trust the
authenticity of qualifications. Verifiable digital credentials, on the other hand, utilize
cryptographic techniques to ensure the integrity and immutability of the credential
data [14]. By implementing secure digital credentialing systems, educational insti-
tutions can mitigate the risk of credential fraud and enhance trust in the qualifica-
tions of their students.
The second reason is enabling lifelong learning. In today’s rapidly evolving job
market, lifelong learning has become essential for individuals to adapt and upskill.
However, the recognition of informal and non-traditional learning experiences
poses a challenge. Verifiable digital credentials can address this challenge by pro-
viding a mechanism to capture and represent various forms of learning, including
online courses, workshops, and work-based learning [3]. These digital credentials
can be easily updated, stacked, and shared, enabling individuals to showcase their
continuous learning journey.
3.2
Exploring the Use of Blockchain for Storing
and Authenticating Credentials
Blockchain technology has garnered significant attention in recent years, not only
for its association with cryptocurrencies but also for its potential to revolutionize
various industries. One area where blockchain shows great promise is in the storage
and authentication of credentials. We use blockchain for the purposes on Fig. 1.2
and highlight its benefits and challenges.
Fig. 1.2 The use of BCT in storage and authentication of credentials
A. Marouan et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-19-320.jpg)
![8
redundancy and fault tolerance. This decentralized approach reduces the risk of a
single point of failure, making it challenging for hackers to compromise the data.
Additionally, storing multiple copies of data across the network enhances data avail-
ability and integrity.
Immutability and Data Integrity: Blockchain achieves data immutability through
the use of cryptographic hashing algorithms [16] and consensus mechanisms. Each
data block is cryptographically linked to the previous block, forming a chain of
blocks. Once a block is added to the chain, it becomes computationally infeasible to
alter or delete its contents without invalidating the entire chain. This feature ensures
data integrity, as any unauthorized modification attempts can be easily detected.
Encryption and Access Control: Blockchain technology can leverage advanced
encryption techniques [7] to protect the confidentiality of stored data. By encrypting
data before storing it on the blockchain, sensitive information remains secure even
if the blockchain’s content is publicly accessible.Additionally, access control mech-
anisms, such as public-private key pairs, can be implemented to grant authorized
parties the ability to decrypt and access specific data.
Smart Contracts and Data Management: Smart contracts are self-executing
agreements with predefined rules and conditions stored on the blockchain. They
provide an additional layer of security and automation for data storage and access.
Smart contracts can enforce access controls, verify data integrity, and execute pre-
defined actions based on specified conditions. By leveraging smart contracts,
blockchain-
based data storage systems can ensure secure and reliable data manage-
ment [19, 23].
5 Challenges and Limitations
5.1
Technical Barriers in Implementing Blockchain
in Education
While blockchain technology holds significant potential for transforming the educa-
tion sector, there are several technical barriers that need to be addressed for success-
ful implementation. These challenges can impact the scalability, interoperability,
and integration of blockchain solutions within existing educational systems.
One of the primary technical barriers is scalability. As blockchain networks grow
in size and complexity, the computational and storage [10] requirements increase
significantly. Public blockchains, such as Bitcoin and Ethereum, face scalability
challenges in terms of transaction throughput and latency. This can pose a hindrance
when it comes to processing a large volume of educational data, such as student
records, certifications, and assessments. Several scalability solutions, including
sharding and off-chain transactions, are being explored to address this challenge
and improve the performance of blockchain networks.
A. Marouan et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-21-320.jpg)
![9
Interoperability [20] is another technical hurdle in implementing blockchain in
education. Educational institutions typically use a wide range of systems and plat-
forms to manage student data, learning management systems, and other educational
resources. Achieving seamless integration between blockchain and these existing
systems is crucial to enable effective data exchange and interoperability. Efforts are
underway to develop standardized protocols, such as the InterPlanetary File System
(IPFS) and the W3C Verifiable Credentials, to facilitate interoperability between
different blockchain networks and educational platforms.
Security and privacy considerations also present technical challenges. While
blockchain technology itself provides strong security through cryptography and
immutability, securing the underlying infrastructure, such as storage, communica-
tion channels, and user identities [13], is critical. Protecting sensitive student data,
such as personally identifiable information (PII), from unauthorized access or data
breaches requires robust security measures. Additionally, ensuring data privacy
while leveraging the transparency and traceability of blockchain technology requires
careful design and implementation.
Furthermore, user experience and accessibility are important aspects to consider.
The usability of blockchain applications and interfaces should be intuitive and user-
friendly, ensuring that educators, students, and administrators can easily interact
with the blockchain system. The technical complexity [12] associated with block-
chain technology should be abstracted to provide a seamless user experience,
enabling widespread adoption within educational settings.
Addressing these technical barriers requires collaboration between educational
institutions, technology providers, and blockchain experts. Research and develop-
ment efforts are underway to create scalable blockchain solutions, enhance interop-
erability, strengthen security measures, and improve user experience in educational
blockchain applications.
5.2
Regulatory Considerations and Compliance Issues
Implementing blockchain technology in the education sector requires careful atten-
tion to regulatory considerations and compliance issues. As blockchain solutions
involve the storage and management of sensitive student data, educational institu-
tions must navigate legal and regulatory frameworks to ensure compliance with
relevant laws and regulations.
One significant regulatory consideration is data protection and privacy. Many
jurisdictions have stringent data protection laws, such as the European Union’s
General Data Protection Regulation (GDPR) and the California Consumer Privacy
Act (CCPA). These regulations impose strict requirements for the collection, stor-
age, and processing of personal data, including student information. Educational
institutions [5] adopting blockchain must ensure that their blockchain implementa-
tions adhere to these regulations, providing appropriate consent mechanisms, data
minimization, and secure data handling practices.
1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-22-320.jpg)
![10
In addition to data privacy, compliance with academic standards and accredita-
tion requirements is essential. Educational institutions must ensure that blockchain-
based systems for storing and verifying credentials meet the standards set by
relevant accrediting bodies. This involves establishing trust in the blockchain sys-
tem and ensuring the accuracy and integrity of the stored credentials. Collaborating
with accrediting bodies and regulatory agencies can help ensure that blockchain
implementations in education comply with the necessary standards and regula-
tions [6].
Furthermore, intellectual property rights and copyright considerations should not
be overlooked. Blockchain technology [11] enables the transparent sharing and dis-
tribution of educational content, raising questions about ownership and licensing
rights. Educational institutions [15] must navigate copyright laws and establish
clear guidelines regarding the use and distribution of educational materials on the
blockchain. Collaborations with content creators, licensing agencies, and legal
experts can help address these regulatory challenges effectively.
It is important to recognize that regulatory frameworks surrounding blockchain
technology in education are still evolving. As such, educational institutions should
actively monitor updates and engage in discussions with policymakers and regula-
tory authorities to shape the regulatory landscape and ensure compliance with
emerging requirements.
5.3
Scalability Challenges and Potential Solutions
Scalability is a significant challenge when implementing blockchain technology, as
it involves handling a large volume of transactions and data within a network. This
challenge becomes particularly crucial in sectors like education, where the storage
and processing of extensive student records, certificates, and assessments are
involved. Addressing scalability concerns is crucial to ensure that blockchain-based
educational systems can handle increased transactional demands and accommodate
growing user bases [1].
One of the primary scalability challenges in blockchain technology is transaction
throughput. Public blockchains, such as Bitcoin and Ethereum, have limitations in
terms of the number of transactions they can process per second. For instance,
Bitcoin can handle around 7 transactions per second, while Ethereum’s throughput
[9] is higher but still limited. This limitation can result in delays and increased trans-
action fees, impeding the seamless flow of data and transactions within an educa-
tional blockchain ecosystem.
To overcome these challenges, several potential solutions are being explored.
One approach is the implementation of off-chain transactions or layer-2 scaling
solutions. These solutions involve conducting certain transactions or computations
off the main blockchain, reducing the load on the main network. Off-chain transac-
tions can be settled periodically on the main blockchain, maintaining the security
and immutability of the underlying data.
A. Marouan et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-23-320.jpg)
![11
Another solution is the concept of sharding, which involves dividing the block-
chain network into smaller, interconnected sub-networks known as shards. Sharding
allows for parallel processing of transactions and data across multiple shards, sig-
nificantly increasing the overall capacity and transaction throughput of the block-
chain network. By distributing the workload across shards, scalability can be
improved while maintaining the security and decentralization aspects of the
blockchain.
Additionally, advancements in consensus algorithms offer potential scalability
improvements. Traditional proof-of-work algorithms, while secure, are computa-
tionally intensive and limit scalability. Alternative consensus mechanisms [18] like
proof-of-stake, proof-of-authority, or delegated proof-of-stake can provide higher
transaction throughput and lower energy consumption, enabling greater scalability
for blockchain networks.
Furthermore, advancements in infrastructure, such as high-performance hard-
ware and optimized software, can contribute to improved scalability. Increasing net-
work bandwidth, storage capabilities, and computational power can help handle
larger volumes of data and transactions within a blockchain system.
It is important to note that scalability solutions for blockchain technology are
still evolving, and their effectiveness and applicability may vary depending on the
specific use case and requirements of educational blockchain implementations.
Ongoingresearchanddevelopmenteffortscontinuetoexploreinnovativeapproaches
to address scalability challenges in blockchain technology.
6
Future Research and Experimentation
6.1
Importance of Continued Research and Experimentation
Continued research and experimentation play a vital role in unlocking the full
potential of blockchain technology in the education sector. As blockchain is a rela-
tively new and evolving technology, there are still many aspects to explore, refine,
and optimize. Investing in research and experimentation allows educational institu-
tions to stay at the forefront of innovation and harness the transformative power of
blockchain in education.
One key importance of continued research is to address technical challenges and
limitations associated with blockchain implementation. Researchers can focus on
scalability, interoperability, security, and privacy issues to develop solutions that
overcome these hurdles. Through experimentation, researchers can test new consen-
sus algorithms, explore novel approaches to data storage and management, and pro-
pose frameworks for secure and efficient integration of blockchain with existing
educational systems.
Moreover, research efforts can help identify and understand the potential benefits
and impacts of blockchain technology in education. Studies can evaluate the
1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-24-320.jpg)
![13
over their educational data. This empowers learners to share their achievements
with potential employers, institutions, or other stakeholders, eliminating the need
for intermediaries and enhancing data privacy.
Furthermore, blockchain-based microcredentialing systems have the potential to
revolutionize lifelong learning and skills development. These systems can provide
learners with the ability to earn and store digital badges or tokens for completing
specific courses or acquiring specific skills. These digital credentials can be easily
verified and shared, enabling employers and educational institutions to assess an
individual’s competency and skill set accurately [8].
Despite the promising potential of blockchain in education, further exploration
and refinement are necessary to address challenges and ensure successful imple-
mentation. Issues such as scalability, interoperability, and standardization need to
be carefully considered. Scalability challenges arise due to the decentralized nature
of blockchain networks, as they require significant computational power and stor-
age capacity. Innovative solutions such as off-chain scaling techniques and layer-
two protocols can help overcome these challenges.
Moreover, regulatory considerations and compliance issues play a crucial role in
the adoption of blockchain technology in education. Compliance with data protec-
tion and privacy laws, as well as addressing concerns related to data ownership and
control, is essential. Collaboration between educational institutions, policymakers,
and blockchain technology providers is necessary to establish a robust regulatory
framework that ensures data security and privacy while promoting innovation.
7 Conclusion
This chapter has highlighted the key points and findings regarding the potential of
blockchain technology in education. It has emphasized the benefits of blockchain in
verifying credentials, enabling secure record-keeping, and revolutionizing micro-
credentialing. However, challenges such as scalability need to be addressed for suc-
cessful implementation. Overall, blockchain has the potential to transform education
by enhancing transparency, security, and learner empowerment.
In summary, the implementation of blockchain in education presents various
implications and challenges. While it holds promise for verifying credentials, ensur-
ing data security, and enabling lifelong learning, scalability and regulatory consid-
erations need to be carefully addressed. Overcoming these challenges will be crucial
to fully harnessing the potential benefits of blockchain technology in education.
In final thoughts, blockchain technology has the transformative potential to revo-
lutionize education. Its ability to enhance transparency, security, and ownership of
educational records can empower learners and streamline processes. However, suc-
cessful implementation requires addressing scalability challenges, regulatory con-
siderations, and fostering collaboration among stakeholders.With careful refinement
and exploration, blockchain has the capacity to reshape education, making it more
accessible, efficient, and learner-centric.
1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-26-320.jpg)
![19
3 Blockchain-Enabled Technology in Healthcare
Blockchain technology was initially created to provide support for the use of cryp-
tocurrency. However, recently it has been applied to different fields, achieving
exceptional security [1]. At present, the healthcare industry has begun to integrate
blockchain into several aspects of its operations. Its attributes, such as decentralized
exchanges, micro-transactions, smart contracts, and consensus mechanisms, can
help safeguard the confidentiality of patient data, which is an important asset of the
healthcare sector.
A blockchain is a type of immutable ledger that is distributed and replicates
transactions across its network, incorporating cryptographic links in chronological
order between information. It consists of consensus protocol and smart contracts to
ensure security. It can overcome the obstacles experienced by AI by establishing
trust among users, organizing data, and facilitating resource sharing in AI-based
healthcare. Peer-to-peer networks and decentralized ledger is a feature of block-
chain technology. Transactional records are securely maintained by a distributed
ledger. By logging local gradients on the blockchain, this functionality supports the
safe learning of heterogeneous data. Similarly, the execution of transactions in a
distributed network without the involvement of a third party or centralized authority
is done automatically by a smart contract. It is a piece of executable code that is
present on every node and that is triggered when a transaction is initiated. The trans-
action is validated via smart contracts. Smart contracts enable the imposition of
access control regulations for data access. Smart contracts enable user provenance.
In the case of transactional data, a block is produced. A consensus algorithm is used
by miners to commit the block to the blockchain. Algorithms of consensus mine the
block. It forces miners to work through challenging cryptographic riddles and pub-
lish their solutions with other miners. The opportunity to mine a block of the trans-
actions and adding it to the available chain and duplicating the created chain in all
the connected nodes is given to the miner who solves the challenge first. Consensus
algorithms may be the effective method for group decisions on diagnosis and treat-
ment in AI-based healthcare systems. Blocks are immutable and auditable since
they are cryptographically connected to one another. When the transaction is copied
and duplicated across all network nodes, the highest level of availability and trans-
parency is achieved. Medical data can be verified via cryptographic linking, which
can also provide a tamper-proof duplicate of it. Anyone can join the network and
take part in transactions while using a public blockchain. Private blockchain, on the
other hand, places restrictions on access without sufficient authentication and veri-
fication. Public and private blockchain characteristics are combined in consortium
blockchain. The working of the blockchain is shown in Fig. 2.3.
Figure 2.4 shows some of the applications of blockchain in healthcare. Securing
patient’s medical data and effectively managing various product supply chains,
including those for medical equipment, organs, medicines, drugs components, oxy-
gen cylinders inclusive of all other pharmaceuticals, are two essential criteria of the
healthcare business.
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-32-320.jpg)
![20
Fig. 2.3 Process workflow involved in blockchain
Fig. 2.4 Blockchain and healthcare
Blockchain is a leading-edge technology that has the potential to revolutionize
the healthcare system by providing security, dependability, confidentiality, and
compatibility [2]. It features an unalterable and distributed ledger in which patients’
medical data can be stored securely and prevented from tampering. It is safeguarded
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-33-320.jpg)
![21
by cryptographic elements like hashing, digital signatures, and asymmetric keys
ensuring that the data cannot be tampered with [3]. Since the ledger is decentralized,
any slight alteration to a data transaction will be detected by all blockchain mem-
bers, resulting in greater transparency across the entire system. Using blockchain
technology enables safe medical data transmission, prevents breaches, and effective
management of medical resources as the healthcare sector is constantly at risk of
being attacked. A healthcare system that uses blockchain technology was presented
by the authors of [4] to protect the confidentiality of user data. They also utilized
various mechanisms to safeguard users’ confidential information, developed smart
contracts in order to authenticate transactions of data, and provide control access
and decision-making in an open network. A safe and dependable blockchain-
adapted strategy to prevent security violations of electronic medical record systems
was put forth by Ray et al. in [5]. To enable secure data sharing over the IoT net-
work, they deployed private blockchain and swarm intelligence techniques.
Moreover, Subramanian et al. [6] examined the use of blockchain and AI technol-
ogy in the treatment of diabetes disorders, particularly during the COVID-19 pan-
demic. Similarly, medical facilities, testing facilities, academic institutions, and
patients may share useful information and collaborate to enhance the AI model.
Nevertheless, due to privacy and security issues, they have trouble sharing crucial
data with outside parties. Hence, a barrier to raising the caliber of AI-based health-
care systems is secure data sharing. In order to improve the prediction of lung can-
cer using CT scan pictures, Kumar et al. [7] suggested a method that involves
exchanging regional models through the network of blockchain. Hence, the updated
model assists in precisely diagnosing the ailment of the patients, leading to enhanced
therapy. By preventing actual data sharing, privacy is maintained. Organizations
will exchange local gradients via smart contracts and transfer their data to the IPFS
(Interplanetary File System). The global model is trained using a consensus
approach called Delegated Proof-of-Stake. A smart contract establishes trust in the
data, and the blockchain is updated with the local gradient’s hash. To expedite bio-
medical research, Mamoshina et al. [8] have offered AI and blockchain technolo-
gies. It also provides patient incentives to get regular examinations and benefits
from new technology for managing and making money through their personal infor-
mation. Patients can sell their medical records using tokens on the permissioned
blockchain platform called Exonum, which has been proposed by the group.
Nevertheless, once data has been sold to authorities, this framework has no control
over it. An intrusion detection system has been proposed by Nguyen et al. [9] to
safeguard data transfer in the healthcare industry’s cyber-physical system. Patients
frequently lack control over who has access to their medical data. A safe, immuta-
ble, and decentralized gradient mining is used in place of the insecure central gradi-
ent aggregator on the blockchain. Smart contracts are used to control the edge
computing, management of trust, authentication, and distribution of trained models,
as well as the identification of nodes and the datasets or models used by them. This
method offers total encryption for both a trained model and a dataset. A decentral-
ized AI-powered healthcare system has been built by Puri et al. [10] that can access
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-34-320.jpg)
![22
and verify IoT devices along with fostering trust and transparency in the health
records of patients. This method uses the creation of a public blockchain network
and AI-enabled smart contracts. The framework also finds IoT nodes in the network
that might be harmful. BITS is a special intelligent TS system built on blockchain
that is offered by Gupta et al. [11]. They offer extensive insights into the blockchain-
and cloud-based smart T’s frameworks, highlighting the difficulties with data man-
agement, security, dependability, and secrecy.
To maintain the security and privacy of the IoMT, Polap et al. [12] have provided
distributed learning. It utilizes decentralized learning along with blockchain secu-
rity enabling the creation of intelligent systems that preserves confidentiality by
keeping the data locally stored. The model poisoning attacks can be lessened by
using this approach. Kumar et al. [13] described a method to detect people infected
with COVID-19 through CT images by developing a model jointly using blockchain
technology and federated learning to maintain secrecy. To solve these challenges,
Chained Distributed Machine Learning (C-DistriM), which is a unique decentral-
ized learning that also uses blockchain-based architecture, has been predicted by
Zerka et al. [14] to be made for imaging in the medical field. Blockchain preserves
model integrity and records the unchangeable history of computation. The Explorer
Chain framework, which was proposed by Kuo et al. [15], aims to build a model that
can predict throughout the distributed architecture. The framework employs
machine learning and blockchain technology that does not require patient data shar-
ing or a central coordinating node, making it decentralized and without a central
authority. Similarly, in order to establish the transmission of data, transfer of the
model, and its testing in three places in China and Singapore, Schmetterer et al. [16]
implemented a blockchain-enabled AI technology. A wireless capsule endoscopy
approach for identifying stomach infections was investigated by Khan et al. [17]. A
complex artificial neural network model is secured using a blockchain-based method
to enable accurate diagnosis of gastrointestinal conditions like tumors and bleeding.
Each part includes a separate block that stores specific data to fend off attempts that
would temper or modify it. Natural language processing (NLP) technology, in par-
ticular, has proven an efficient tool to categorize the emotion, and feelings of texts,
present in social media such as posts, according to Pilozzi et al. [18]. These methods
could be applied to learn more about how people see Alzheimer’s disease. Patients
will have more control over their data if decentralized, secure data transit and stor-
age techniques like blockchain are used. Most of the anxieties associated with mis-
takenly revealing personal information to an organization that might treat the patient
unfairly will be eliminated. The work that has been done in blockchain for AI-based
healthcare is shown in Fig. 2.5. It shows the kind of blockchain that is used for the
various data modalities. Blockchain is categorized into three types: consortium, pri-
vate, and public blockchain. The most used public blockchain is Ethereum and the
private is Hyperledger.
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-35-320.jpg)
![23
Public Blockchain
Consortium Blockchain
Private Blockchain
Not mentioned
General Data
Image Data
Text Data
Audio Data
M
o
m
o
s
h
i
n
a
e
t
a
l
.
2
0
1
8
Polap et al.
2020
Zerka et al.
2020
Rahman et al.
2020
Pilozzi et al.
2020
K
i
m
e
t
a
l
.
2
0
2
0
G
up
ta
et
al
.
20
20
K
ha
n
et
al
.
20
21
P
u
r
i
e
t
a
l
.
2
0
2
1
Nguyen et al.
2021
Kumar et al.
2021
Schmetterer et al.
2021
Kumar et al.
2021
K
u
o
e
t
a
l
.
2
0
2
0
J
e
n
n
a
t
h
e
t
a
l.
2
0
2
0
Fig. 2.5 Types of blockchain, data
4
Role of Artificial Intelligence in Smart Healthcare Systems
The healthcare sector demands advanced and anticipatory solutions that offer
boundless prospects for accurate and beneficial patient treatment and management
operations [19]. IoT technologies generate a vague volume of data and transfer it to
and from different parts of the health industry. The health industry needs to imple-
ment AI technologies for the effective management of data and its improvisation.
The employment of different AI devices in the health sector has many benefits over
the current system, which relies on time-consuming data analysis and decision-
making methods. In order to offer insightful information about diagnosis, clinical
decision support, and treatment, it interacts with medical data.
For instance, in [20], scientists looked at the osteoporosis condition, which is
typically identified by conventional X-rays and MRI scans. An AI-featured selec-
tion technique was used by the authors to facilitate the diagnosis of osteoporosis
patients through the data obtained by ultrasound. As a result, they were able to clas-
sify osteoporosis patients’ fracture risk with 71% accuracy. Wazid et al. [21] dis-
cussed the key features ofAI technologies in the healthcare industry. They employed
AI algorithms to effectively forecast the likelihood of myocardial infarction and the
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-36-320.jpg)
![24
possibility of developing tumors while also uncovering insightful patterns in the
medical data. In [22], Parra et al. investigated how AI algorithms could be used for
sustainable development. Here, they looked at the people who required an AI-based
question-recommendation system for various scenarios. The main goal of their
study was to support the suggestion for AI-based questions in the health industry,
having the potential to be used in a vast number of applications beyond security
screening and financial services. Similarly, Tedeschini et al. [23] used federated
learning, a decentralized technique, to create a distributed networking architecture
for segmenting brain tumors based on message queuing telemetry transport
(MQTT). Their findings demonstrate that the suggested framework performs more
accurately and quickly during routine healthcare system activities.
A data processing method called machine learning automatizes the creation of
models capable of analysis. It focuses on enabling computers to study data, find
patterns, and make human-like choices without actual human input. In order to
complete these tasks, validated data had to be obtained. After the classifiers were
successfully trained, the model had to be deployed. Retraining and feedback loops
may be used to continue improving performance. Any attempt to uncover, alter, dis-
able, harm, capture, or collect information by taking advantage of system weak-
nesses constitutes a threat to the device. The fundamental security requirement for
any system is to maintain the privacy of sensitive data or processes. In order to keep
the trained model from malfunctioning, three vitals must also be safeguarded.
The section that comes after Fig. 2.6 focuses on the AI attack surface. Any adver-
sary can attack an AI-based system by targeting data, classifiers/algorithms, and
learning models.
Attack
Data
Adversarial
Attack
Spoofing
Physical
Attack
Trojan/Backdoor
Attack
Timing Side
Channel Attack
Classifier/
Algorithm
Model
Fig. 2.6 Attack surface of artificial intelligence
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-37-320.jpg)
![25
4.1 Data
Data is the raw statistics and facts used by a machine. It is a critical component of
artificial intelligence. Data is required to train current models and development of
all current technologies. It costs a lot of money just to get as much precise data as
you can. The attack’s data-targeting strategy has a significant impact on AI-based
systems. By taking advantage of the extraordinary sensitivity of AI to detect slight
differences in the input, known as a poisoning attack, data can be violated either
during the learning phase or during the filed-test. These assaults may be promoted
via spoofing [24]. It is a type of cyberattack when a malicious party uses a computer,
device, or network to pretend to be someone else in order to trick other computer
networks. Malicious opponents typically cannot access the training phase of the
model. In order to trick a classifier or avoid being detected by a neural network dur-
ing testing, they produce hostile input. These attacks can be of the physical or digital
variety. For this study, we are concentrating on cyberattacks of various kinds. A
digital technique immediately introduces small input perturbations. In this case, the
attacker can take advantage of the system that has been targeted without the detec-
tion system noticing. Concept drift might also result from evasion attacks [25].
Prospective attackers may potentially acquire access to the training datasets and
conduct poisoning attacks, which contaminate the datasets with adversarial sam-
ples. As will be covered in more detail in subsequent parts, adversarial attacks can
cause potential damage to the system.
4.2 Classifier/Algorithm
Classifiers/Algorithms are usually affected by a Back-access Attack. A Trojan
assault undermines the authentic model by incorporating a secret entrance to the
neural network, which is triggered by a specific pattern in the testing data. This will
alter the network using a compromised dataset [26]. Trojan assaults vary from
adversarial attacks even though both only occur during the training phase. An
adversarial attack merely influences the outcome in this scenario rather than forc-
ing the neural network to change itself. But, a trojan assault, causes the network to
change itself because of the poisoned input samples so that it can accurately func-
tion for benign input samples. As a result, the network will only malfunction when
a trojan causes it to. A user may have trouble recognizing the trojan assault [27]. A
trojan attack may result from a SPA (Stealthy Poisoning Attack), which is depen-
dent on a Generalized Adversary Network (GAN) [28]. Another illustration of a
neural trojan assault is Badnet [29]. The situation of a trojan assault is shown in
Fig. 2.7.
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-38-320.jpg)
![26
Fig. 2.7 A sample example of a trojan attack
4.3 Model
The model is affected by timing side-channel attack. Neural networks are suscep-
tible to attacks like timing side channels because of their peculiar qualities of hav-
ing varied execution times depending on the depth of the network. An opponent
can determine the layers of the neural network, by observing how long the model
takes to generate output. This adversary makes use of a regressor that was trained
using various network layer counts and execution timings. The information is then
used to create replica models that have features in common with the original net-
work [30]. The essential elements are retained due to memory access patterns.
Reverse engineering of the CNN model’s structure and weights can be used to leak
information through memory and timing side-channel attacks. The crucial charac-
teristics of a neural network [31], such as the overall layers, the size of each layer,
and the interdependencies among them, are exploded by the memory access
patterns.
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-39-320.jpg)
![27
5
Machine Intelligence Technologies in Healthcare
5.1 Text-Based AI Technology
The text-based AI technology widely used is natural language processing. It is a
machine learning model, which aims to enable computers to understand, process,
and generate human-like text and language. The goal of NLP is to build systems and
algorithms that can perform a variety of tasks involving human languages, such as
text classification, machine translation, sentiment analysis, recognition of speech,
and natural language generation.
5.1.1 Applications in Healthcare
Clinical Decision Support Systems (CDDS) receive a variety of inputs, including
incomplete structured data like XML files, structured data like HER, and unstruc-
tured data like diagnostic summaries and progress records. To aid clinical decisions,
various systems have been introduced that utilize NLP techniques that take input
from unstructured data, specifically for the purpose of calculating and automating
diagnoses or treatments. With the help of NLP, CDSS can create results and recom-
mendations that help healthcare practitioners make the best decisions possible by
automatically extracting key information from free text [32, 33]. NLP makes it easy
to extract important clinical information from unstructured data in medical records,
such as physician notes, discharge summaries, and diagnostic reports. This can help
with coding, billing, and clinical decision-making. A sentiment score system has
been used to assess sentiment statements of admission and discharge in a hospital
[34]. Unstructured reports are also used for radiology. NLP enables the recognition
of key aspects in those reports, their extraction, and conversion into usable com-
puter formats [35]. The analysis of vast amounts of free-text medical reports using
NLP contains the potential to contribute to the development of procedure-intensive
fields such as Hepatology. Also, NLP can be used to develop chatbots and virtual
assistants that can answer patient questions and provide basic medical advice. This
can help patients access healthcare information quickly and easily [36]. In a recent
study, NLP was used to categorize diseases and conditions which were challenging
to identify through simple clinical procedures. Using NLP-based solutions for
information retrieval (IR) reduces the time and effort required, ultimately promot-
ing the therapy [37, 38].
5.1.2 Adversarial and Defense Attack
An alteration in the text’s semantics, grammar, or visual similarity that deceives
NLP is known as adversarial text. The techniques for creating hostile text are shown
in Fig. 2.8. To impact the model’s prediction, text-based adversarial examples can
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-40-320.jpg)
![28
Fig. 2.8 Taxonomy of adversarial attack on text
Adversarial Attack in healthcare application
NLP
Normal Text
Risk:High
Adversarial
Attack
Enlargement of the
Heart
Shortness of Breath
Irregular heartbeat
Adversarial Text
Risk:Low
Expansion of the heart
Shortness of Respiration
Non-Uniform Heartbeat
Fig. 2.9 Adversarial attack on NLP-based healthcare application
be created by making slight changes to the text, mimicking real typing errors made
by humans. These modifications are intended to be minimal yet effective in altering
the model’s output. After the attack, the text appears fairly near to the original. This
method of attack is used in assaults like hot flip [38], textbugger [39], and
DeepWordBug [40]. The process of making hostile writing involves paraphrasing
the source material. The semantic equivalent of the original text will be produced by
this assault, but the model’s result for the original text and the modified text will be
different.
Figure 2.9 shows how NLP-based healthcare is affected by adversarial attacks.
The NLP can be tricked by simply substituting synonyms for words while keeping
the text’s semantics. The incorrect diagnosis ultimately results in the incorrect treat-
ment, endangering lives.
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-41-320.jpg)
![31
Blockchain Network
Consensus
Algorithm
NLP data
Text
Node 1 Node 2 Node 3 Node 4
Protect
NLP
model
Protect
NLP
Classifier
Global
market
Distributed
ledger
Local
Gradients
Local NLP
Models
Smart Contract
(Initiate Training)
Fig. 2.11 Protection of training phase of NLP using blockchain
perceptible data from the world [41]. It involves developing algorithms and tech-
niques to enable computers to recognize and classify objects, understand scenes,
track motion, and more using images and videos. The techniques involved in com-
puter vision are object detection, image classification, object tracking, and semantic
and instance segmentation.
5.2.1 Applications in Healthcare
The interpretation and analysis of many types of real-world data are aided by intel-
ligent intervention employing a brain-like structure and advanced technologies like
machine learning and computer vision [42]. A scientific application of machine
learning is computer vision, which uses collected sequences of movies and photos
to identify things. Convolutional neural networks (CNNs), a machine learning algo-
rithm created to analyze picture input, prioritizes different elements to identify one
image from another. Similar to the connection pattern of neurons in the brain, CNNs
have a structural design. Computers have long been able to analyze visual imagery
in meaningful ways thanks to computer vision. Object classification, localization,
and detection are the terms used to describe the processes of determining an object’s
kind, location within an image, and both concurrently [43].
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-44-320.jpg)
![32
Fig. 2.12 Solution for NLP-based healthcare using blockchain technology
According to the National Cancer Institute’s National Lung Screening Trial
(NLST), low-dose CT, which is used for screening lung cancer, has caused a 20%
reduction in mortality [44]. The use of smart monitoring has increased because of
developments in computer vision. To anticipate generalized anxiety disorder (GAD),
a new system using computer vision and ML is introduced [45]. Computer vision
algorithms in an adult intensive care unit can recognize patient movement actions
like getting the patient in and out of bed or a chair [46]. A substantial possibility
exists for deep convolutional neural networks as a tool for ear-related diagnosis
[47]. A comprehensive image processing system to forecast the viability of human
embryos, researchers coupled computer vision methodologies with machine learn-
ing and different techniques involving neural networks [48]. Using a computer
vision technique, it is also possible to identify hip fractures from pelvic X-rays [49].
Recorded endoscopic pictures will be swiftly and precisely analyzed by the ground-
breaking CNN approach to detect esophageal cancer [50]. Deep learning methods
also enable the detection of intracranial hemorrhage (ICH) [51]. It is possible to
make a diagnosis based on chest CT pictures, leading to a machine learning algo-
rithm in a quick and automated diagnostic technique [52]. In order to decrease the
chances of infection from the doctor to the patient COVID-19, a revolutionary
visual SLAM algorithm may also follow and find robots in real-time environ-
ments [53].
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-45-320.jpg)
![33
5.2.2 Adversarial and Defense Attacks
The images in which pixels are purposefully disturbed to confuse and deceive mod-
els while appearing correct to human sight are adversarial images. Adversarial
images trick DNN because it is vulnerable to even the smallest input disturbance.
Figure 2.13 displays the ways adversarial attacks can be done on an image.
Several attack strategies, such as FGSM [54], BIM [55], and R + FGSM [56],
cause the ML model to make wrong predictions and decreases the overall robust-
ness of the model. Figure 2.14 provides an example of an adversarial attack on an
X-ray image.
5.2.3
Blockchain Solutions for Computer Vision–Based Healthcare
Data Layer
Since adversarial attacks are more likely to target images, we frame solutions that
emphasize preventive actions, as seen in Fig. 2.15. A blockchain-based system will
be used to post images to IPFS. A file-sharing technique called IPFS can be used to
store and transport large data. It uses cryptographic hashes, which can be stored in
the blockchain easily. The generated hashes are utilized to ensure the authenticity of
images. First, different hospitals and diagnostic centers that have the data will
request to upload medical images on IPFS, which will be validated by a smart con-
tract before uploading. Every image will have its unique hash, which will be stored
in the blockchain. Users can approach IPFS with the hash code to access the image
data set when needed. The detection of adversarial images will be easy as hash
codes are extremely sensitive. In this way, the data set is secured at IPFS using
blockchain.
Learning Phase
Computer vision uses dynamic data for visual inputs. The model is trained with
image data sets. As shown in Fig. 2.16, security can be provided through block-
chain. With proper authentication, the training in the research centers should be
Fig. 2.13 Adversarial attack on image
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-46-320.jpg)
![36
Initiate Access
to model
Distributed
Ledger
Distributed
Ledger
Learned
Featured Vector
User with
image input Knowledge
Meta Data
Comprehensive
Diagnosis with
explanation
Fig. 2.17 Blockchain security for a trained model of computer vision
5.3.1 Applications in Healthcare
Numerous software applications employ sophisticated AI algorithms and process
digital signals to identify complete sleep schedules, track the rate of breathing,
detect gasping and snorting, and recognize patterns of sleep apnea. These applica-
tions then utilize this information to accurately measure a person’s respiratory rate
while they are sleeping, all via smartphones. These applications combine active
sonar and passive acoustic analysis. One of the application frameworks is “Firefly”
[57]. A framework designed using neural networks (NNs) can distinguish between
four different forms of auscultatory noises, including wheezes, rhonchi, fine crack-
les, and coarse crackles, which reduces human mistakes during auscultation [58].
Researchers have developed classifiers using this technology that can distinguish
between different respiratory illnesses in adults using the auditory features of
coughs. Also, they have created synthetic cough samples for each significant respi-
ratory ailment, using recent advancements. To help doctors, machine learning algo-
rithms identify the earliest stages of pulmonary disease, for example, Cough GAN
generates simulated coughs that mimic major pulmonary symptoms. By accurately
and early diagnosing advanced respiratory illnesses such as chronic obstructive pul-
monary disease, doctors will create the best preventative treatment programs and
lower morbidity [59]. AI-based technologies are used for pediatric breath sound
classification where the use of a CNN architecture (N-CNN) along with other CNN
architectures can be applied to examine discomfort in babies through their sound of
P. Shah et al.](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-49-320.jpg)
![37
crying patterns. Results show that this method is a much more beneficial and viable,
alternate to the method of evaluation used conventionally [60].
5.3.2 Adversarial Attack
Adversarial audio is any audio that contains disturbance, or noise, often known as
adverse perturbations, and it can fool a variety of sound classification systems.
Figure 2.18 shows the classification of adversarial attacks in audio signals. Some
assaults aim to create an adversarial audio sample that closely resembles the origi-
nal, but the learned model would classify it incorrectly. These assaults fall under the
category of speech-to-label assaults. By using the actual audio and the required
output label, the attacker can show how genetic algorithms can generate hostile
audio samples without the use of gradients. It increases random noise while prevent-
ing human awareness of it [61]. During the conversion of speech to text through
acoustic processing, an adversary can attempt to manipulate the output to achieve a
specific result. Such attacks are referred to as speech-to-text attacks. It is possible to
alter the audio spectrum to obtain a desired output by introducing a minor distur-
bance using optimization-based attacks [62]. Figure 2.19 provides an example of an
adversarial attack on an acoustic technology application in the healthcare system.
5.3.3
Blockchain Solutions for Acoustic AI-Based Healthcare
Data Layer
Data resides locally on the computers of data owners, such as physicians, hospital
staff, and laboratories database, similar to NLP-based healthcare. Figure 2.20 dem-
onstrates the data layer construction using blockchain for acoustic AI-based health-
care. As there are many IoMT technologies that can threaten the privacy of entities
Fig. 2.18 Classification of adversarial attack on audio
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-50-320.jpg)
![41
Fig. 2.23 Blockchain for AI-based healthcare explained
References
1. Yaeger, K., Martini, M., Rasouli, J., Costa, A. (2019). Emerging blockchain technology
solutions for modern healthcare infrastructure. Journal of Scientific Innovation in Medicine,
2, 1–7. [CrossRef].
2. Gupta, R., Reebadiya, D., Tanwar, S., Kumar, N., Guizani, M. (2021). When blockchain
meets edge intelligence: Trusted and security solutions for consumers. IEEE Network, 35,
272–278. https://doi.org/10.1109/MNET.001.2000735. [CrossRef].
3. Kumari, A., Gupta, R., Tanwar, S., Tyagi, S., Kumar, N. (2020). When Blockchain meets
smart grid: Secure energy trading in demand response management. IEEE Network, 34,
299–305. https://doi.org/10.1109/MNET.001.1900660. [CrossRef].
4. Wu, G., Wang, S., Ning, Z., Zhu, B. (2022). Privacy-preserved electronic medi-
cal record exchanging and sharing: A blockchain-based smart healthcare system. IEEE
Journal of Biomedical and Health Informatics, 26, 1917–1927. https://doi.org/10.1109/
JBHI.2021.3123643. [CrossRef] [PubMed].
5. Rather, I. H., Idrees, S. M. (2021). Blockchain technology and its applications in the health-
care sector. In Blockchain for healthcare systems (pp. 17–25). CRC Press.
6. Subramanian, G., Sreekantan Thampy, A. (2021). Implementation of Blockchain con-
sortium to prioritize diabetes patients’ healthcare in pandemic situations. IEEE Access, 9,
162459–162475. https://doi.org/10.1109/ACCESS.2021.3132302. [CrossRef].
2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…](https://image.slidesharecdn.com/29267293-250524162625-818fdd8a/85/Blockchain-Transformations-1st-Edition-Sheikh-Mohammad-Idrees-54-320.jpg)
Blockchain Transformations 1st Edition Sheikh Mohammad Idrees
- 1. Blockchain Transformations 1st Edition Sheikh Mohammad Idrees download https://ebookbell.com/product/blockchain-transformations-1st- edition-sheikh-mohammad-idrees-58534586 Explore and download more ebooks at ebookbell.com
- 2. Here are some recommended products that we believe you will be interested in. You can click the link to download. Transformations Through Blockchain Technologythe New Digital Revolution https://ebookbell.com/product/transformations-through-blockchain- technologythe-new-digital-revolution-60152872 Kickstart Blockchain And Cryptography Fundamentals Navigate The Intricacies Of Modern Blockchain Cryptography Smart Contracts And Hyperledger For Driving Realworld Transformations Prof Veerendra Kumar Jain https://ebookbell.com/product/kickstart-blockchain-and-cryptography- fundamentals-navigate-the-intricacies-of-modern-blockchain- cryptography-smart-contracts-and-hyperledger-for-driving-realworld- transformations-prof-veerendra-kumar-jain-60451044 Blockchain And Health Transformation Of Care And Impact Of Digitalization Jan Veuger https://ebookbell.com/product/blockchain-and-health-transformation-of- care-and-impact-of-digitalization-jan-veuger-49417846 Business Transformation Through Blockchain Volume I Horst Treiblmaier https://ebookbell.com/product/business-transformation-through- blockchain-volume-i-horst-treiblmaier-7314142
- 3. Business Transformation Through Blockchain Volume Ii Horst Treiblmaier https://ebookbell.com/product/business-transformation-through- blockchain-volume-ii-horst-treiblmaier-7314144 Digital Health Transformation With Blockchain And Artificial Intelligence Chinmay Chakraborty https://ebookbell.com/product/digital-health-transformation-with- blockchain-and-artificial-intelligence-chinmay-chakraborty-47201980 Blockchain In Supply Chain Digital Transformation Trevor Clohessy https://ebookbell.com/product/blockchain-in-supply-chain-digital- transformation-trevor-clohessy-48838686 Blockchain Of Things And Deep Learning Applications In Construction Digital Construction Transformation Faris Elghaish https://ebookbell.com/product/blockchain-of-things-and-deep-learning- applications-in-construction-digital-construction-transformation- faris-elghaish-43877436 Digital Fluency Understanding The Basics Of Artificial Intelligence Blockchain Technology Quantum Computing And Their Applications For Digital Transformation 1st Ed Volker Lang https://ebookbell.com/product/digital-fluency-understanding-the- basics-of-artificial-intelligence-blockchain-technology-quantum- computing-and-their-applications-for-digital-transformation-1st-ed- volker-lang-23942238
- 5. Signals and CommunicationTechnology Sheikh Mohammad Idrees Mariusz Nowostawski Editors Blockchain Transformations Navigating the Decentralized Protocols Era
- 6. Signals and Communication Technology Series Editors Emre Celebi, Department of Computer Science University of Central Arkansas Conway, AR, USA Jingdong Chen, Northwestern Polytechnical University Xi'an, China E. S. Gopi, Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli, Tamil Nadu, India Amy Neustein, Linguistic Technology Systems Fort Lee, NJ, USA Antonio Liotta, University of Bolzano Bolzano, Italy Mario Di Mauro, University of Salerno Salerno, Italy
- 7. This series is devoted to fundamentals and applications of modern methods of signal processing and cutting-edge communication technologies. The main topics are information and signal theory, acoustical signal processing, image processing and multimedia systems, mobile and wireless communications, and computer and communication networks. Volumes in the series address researchers in academia and industrial R&D departments. The series is application-oriented. The level of presentation of each individual volume, however, depends on the subject and can range from practical to scientific. Indexing: All books in "Signals and Communication Technology" are indexed by Scopus and zbMATH For general information about this book series, comments or suggestions, please contact Mary James at mary.james@springer.com or Ramesh Nath Premnath at ramesh.premnath@springer.com.
- 8. Sheikh Mohammad Idrees Mariusz Nowostawski Editors Blockchain Transformations Navigating the Decentralized Protocols Era
- 9. ISSN 1860-4862 ISSN 1860-4870 (electronic) Signals and Communication Technology ISBN 978-3-031-49592-2 ISBN 978-3-031-49593-9 (eBook) https://doi.org/10.1007/978-3-031-49593-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable. Editors Sheikh Mohammad Idrees Researcher DSE Lab Department of Computer Science (IDI) Norwegian University of Science and Technology Gjøvik, Norway Mariusz Nowostawski Associate professor Norwegian University of Science and Technology Gjøvik, Norway
- 10. v The book “Blockchain Transformations: Navigating the Decentralized Protocols Era” is a go-to guide, revealing how the amazing technology known as blockchain is reshaping various aspects of our lives—from education and health to banking and beyond. In a world that’s always changing with technology, blockchain emerges as a real game-changer. This book is not just about the technology itself, but about the incredible transformations it brings to different aspects of our lives. This book will take you on a journey with blockchain through education, healthcare, digital iden- tity, and more, revealing the potential for positive change. Each chapter is a window into the practical applications and real-world impacts of blockchain technology. This book is for everyone—whether you’re a curious learner, a tech enthusiast, or a professional seeking insights into the next wave of innovation. This book will take you on a trip to explore how this technology is making our world better. Let’s dive in together into these chapters, explore the exciting world of decentralization, and discover new possibilities. – – Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong Learning Embark on a journey through the educational realm as we unveil the secure and lifelong learning opportunities facilitated by blockchain technology. – – Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare Security Explore the intersection of blockchain and artificial intelligence, unraveling the enhanced security measures in healthcare through innovative applications. – – Decentralized Key Management for Digital Identity Wallets Delve into the ecosphere of digital identity management as we navigate through decentralized key solutions in the realm of blockchain. – – Towards Blockchain-Driven Solution for Remote Healthcare Service: An Analytical Study Conduct a critical analysis of blockchain-driven solutions, particularly focusing on remote healthcare services and their transformative impact. Introduction
- 11. vi – – Smart Contract Vulnerabilities: Exploring the Technical and Economic Aspects Uncover the technical and economic aspects surrounding smart contract vulner- abilities, offering insights into potential pitfalls and safeguards. – – Modernizing Healthcare Data Management: A Fusion of Mobile Agents and Blockchain Technology Witness the fusion of mobile agents and blockchain technology in revolutioniz- ing healthcare data management, ensuring efficiency and security. – – Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges Investigate the synergy between machine learning and blockchain in ensuring the security of the Internet of Things (IoT) and address current challenges. – – Decentralized Identity Management Using Blockchain Technology: Challenges and Solutions Navigate through the challenges and innovative solutions in decentralized iden- tity management, highlighting the role of blockchain technology. – – Reshaping the Education Sector of Manipur Through Blockchain Witness the transformative impact of blockchain on the education sector, with a focus on reshaping the landscape in Manipur. – – Exploring the Intersection of Entrepreneurship and BlockchainTechnology: A Research Landscape Through R Studio and VOSviewer Embark on a research journey exploring the intersection of entrepreneurship and blockchain, utilizing R Studio and VOS-viewer for a comprehensive landscape. – – Transforming Educational Landscape with Blockchain Technology: Applications and Challenges Uncover the applications and challenges associated with transforming the educa- tional landscape through the integration of blockchain technology. – – Verificate: Transforming Certificate Verification Using Blockchain Technology Explore the innovative Verificate system, revolutionizing certificate verification through the seamless integration of blockchain technology. – – Transforming Waste Management Practices Through Blockchain Innovations Witness the positive environmental impact of blockchain innovations in trans- forming waste management practices. – – Decentralized Technology and Blockchain in Healthcare Administration Explore the decentralized technologies reshaping the landscape of healthcare administration, with a primary focus on blockchain applications. – – Blockchain Technology Acceptance in Agribusiness Industry Delve into the acceptance and integration of blockchain technology in the agri- business industry, revolutionizing traditional practices. – – AdoptionofBlockchainTechnologyandCircularEconomyPracticesbySMEs Analyze the adoption of blockchain technology and its alignment with circular economy practices among small- and medium-sized enterprises (SMEs). Introduction
- 12. vii Contents 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong Learning�������������������������������������������������������� 1 Adil Marouan, Morad Badrani, Nabil Kannouf, and Abdelaziz Chetouani 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare Security������������������������������������������������ 15 Pranay Shah, Sushruta Mishra, and Angelia Melani Adrian 3 Decentralized Key Management for Digital Identity Wallets�������������� 47 Abylay Satybaldy, Anushka Subedi, and Sheikh Mohammad Idrees 4 Towards Blockchain Driven Solution for Remote Healthcare Service: An Analytical Study������������������������������������������������������������������ 59 Siddhant Prateek Mahanayak, Barat Nikhita, and Sushruta Mishra 5 Smart Contract Vulnerabilities: Exploring the Technical and Economic Aspects ���������������������������������������������������������������������������� 81 Deepak Dhillon, Diksha, and Deepti Mehrotra 6 Modernizing Healthcare Data Management: A Fusion of Mobile Agents and Blockchain Technology�������������������������������������� 93 Ashish Kumar Mourya, Gayatri Kapil, and Sheikh Mohammad Idrees 7 Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges���������������������������������� 107 P. Hemashree, V. Kavitha, S. B. Mahalakshmi, K. Praveena, and R. Tarunika
- 13. viii 8 Decentralized Identity Management Using Blockchain Technology: Challenges and Solutions�������������������������������������������������� 131 Ahmed Mateen Buttar, Muhammad Anwar Shahid, Muhammad Nouman Arshad, and Muhammad Azeem Akbar 9 Reshaping the Education Sector of Manipur Through Blockchain������������������������������������������������������������������������������������������������ 167 Benjamin Kodai Kaje, Ningchuiliu Gangmei, Hrai Dazii Jacob, and Nganingmi Awungshi Shimray 10 Exploring the Intersection of Entrepreneurship and Blockchain Technology: A Research Landscape Through R Studio and VOSviewer�������������������������������������������������������� 181 Nisha Kumari, Bangar Raju Indukuri, and Prajeet Ganti 11 Transforming Educational Landscape with Blockchain Technology: Applications and Challenges �������������������������������������������� 197 Roshan Jameel, Bhawna Wadhwa, Alisha Sikri, Sachin Singh, and Sheikh Mohammad Idrees 12 Verificate – Transforming Certificate Verification Using Blockchain Technology ���������������������������������������������������������������� 211 Tanmay Thakare, Tanay Phatak, Gautam Wadhani, Teesha Karotra, and R. L. Priya 13 Transforming Waste Management Practices Through Blockchain Innovations �������������������������������������������������������������������������� 221 Ritu Vats and Reeta 14 Decentralized Technology and Blockchain in Healthcare Administration ���������������������������������������������������������������������������������������� 229 Anamika Tiwari, Alisha Sikri, Vikas Sagar, and Roshan Jameel 15 Blockchain Technology Acceptance in Agribusiness Industry������������ 239 C. Ganeshkumar, Arokiaraj David, and Jeganthan Gomathi Sankar 16 Adoption of Block Chain Technology and Circular Economy Practices by SMEs������������������������������������������������������������������ 261 Mukesh Kondala, Sai Sudhakar Nudurupati, and K. Lubza Nihar Index������������������������������������������������������������������������������������������������������������������ 273 Contents
- 14. 1 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. M. Idrees, M. Nowostawski (eds.), Blockchain Transformations, Signals and Communication Technology, https://doi.org/10.1007/978-3-031-49593-9_1 Chapter 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong Learning Adil Marouan, Morad Badrani, Nabil Kannouf, and Abdelaziz Chetouani 1 Introduction 1.1 Background on Blockchain Technology and Its Applications Beyond Finance Blockchain technology, initially developed for cryptocurrencies like Bitcoin, has garnered widespread attention due to its potential to revolutionize various industries beyond finance. Blockchain is a decentralized and distributed ledger that records transactions across multiple computers, ensuring transparency, immutability, and security. While finance was the initial domain where blockchain gained prominence, its applications have expanded to numerous sectors, including education [16, 22]. Blockchain technology provides several unique features [17] that make it suit- able for applications beyond finance. One of the key features is decentralization, which means that no single entity has control over the entire blockchain network. Instead, the network participants, known as nodes, collectively maintain and vali- date the transactions and records. This decentralized nature eliminates the need for intermediaries, reducing costs and increasing efficiency. Another crucial aspect of blockchain is immutability. Once a transaction or record is added to the blockchain, it cannot be altered or deleted. This feature ensures the integrity and trustworthiness of the data stored on the blockchain, mak- ing it highly resistant to tampering and fraud. Immutability is achieved through A. Marouan (*) · M. Badrani · A. Chetouani LaMAO Laboratory, ORAS team, ENCG, Mohammed First University, Oujda, Morocco e-mail: adil.marouan@ump.ac.ma; m.badrani@ump.ac.ma; a.chetouani@ump.ac.ma N. Kannouf LSA laboratory, SOVAI Team, ENSA, Abdelmalek Essaadi University, Alhoceima, Morocco
- 15. 2 cryptographic techniques and consensus algorithms, ensuring that all network par- ticipants agree on the validity of transactions [4, 24]. Furthermore, blockchain technology offers enhanced security. Data stored on the blockchain is encrypted and linked to previous transactions, creating a chain of blocks that are nearly impossible to manipulate without consensus from the net- work. Additionally, the decentralized nature of the blockchain reduces the risk of a single point of failure and makes it more resilient against cyberattacks. Beyond finance [2], blockchain technology has the potential to transform the field of education. By leveraging its unique features, blockchain can address various challenges related to data privacy, security, verification, and accessibility in education. 1.2 Importance of Exploring Blockchain Technology in Education Blockchain technology has emerged as a transformative force across various indus- tries, and its potential in the field of education is gaining significant attention. Blockchain, often associated with cryptocurrencies like Bitcoin, is essentially a decentralized and transparent digital ledger that records and verifies transactions. However, its application extends far beyond financial systems, offering unique advantages that can revolutionize the education sector (Fig. 1.1). This chapter aims to explore the potential of BCT in revolutionizing education. It addresses key issues related to data privacy, security, verification, and accessibil- ity within the education system. Blockchain enhances education credentialing and verification with secure storage and reliable methods like MIT’s Digital Diploma, reducing fraud and ensuring trust. Blockchain-based learning records facilitate portable and verified achievements, enabling seamless credit transfer and recognition of prior learning across institutions and industries. Blockchain ensures secure data management in education by leveraging decentralization, cryptographic algorithms, and student-controlled selective sharing, safeguarding student records and sensitive information from breaches. Blockchain’s smart contracts automate administrative tasks in education, improving efficiency, reducing errors, and enabling personalized student support through streamlined enrollment, fee payments, course registrations, and certification issuance. Blockchain enables decentralized learning platforms, fostering direct interaction between students and educators, as exemplified by platforms like Teachur and BitDegree. Fig. 1.1 Applications of BCT in education A. Marouan et al.
- 16. 3 2 Blockchain Technology and Education 2.1 Unique Features of Blockchain That Can Address Educational Challenges Blockchain technology possesses several unique features that have the potential to address various challenges faced in the field of education. This section will explore some of these features and their potential applications in addressing educational challenges. (a) Data Integrity and Security: Blockchain’s inherent design ensures data integrity and security. The immutability of data stored on the blockchain makes it highly resistant to tampering or unauthorized modifications. This feature can be lever- aged to address challenges related to student record management, certificate authentication, and academic credential verification [3], storing educational records and credentials on the blockchain, educational institutions can maintain a reliable and tamper-proof repository of student achievements, ensuring the authenticity and security of educational data [21]. (b) Transparent and Trustworthy System: Blockchain’s transparency and decentral- ized nature create a trustworthy system for educational transactions. Smart con- tracts, self-executing agreements built on blockchain, can facilitate transparent and automated processes in various areas, such as student enrollment, course registration, and financial transactions. These smart contracts can streamline administrative processes, reduce fraud, and enhance trust among stake- holders [2]. (c) Portable and Lifelong Learning Records [14]: Blockchain technology enables the creation of portable and interoperable learning records. Students can have ownership and control over their educational achievements, which can be securely stored on the blockchain. This feature allows for the seamless transfer of learning records between educational institutions, supporting lifelong learn- ing and facilitating credential recognition. (d) Microcredentialing and Personalized Learning: Blockchain can enable the issu- ance and management of microcredentials, which are digital badges represent- ing specific skills or competencies. Microcredentials can be verified and shared securely, allowing individuals to demonstrate their skills beyond traditional degrees or certifications. This supports personalized learning pathways, enabling learners to showcase their diverse skills and achievements. (e) Enhanced Collaboration and Content Sharing: Blockchain technology can facilitate decentralized and peer-to-peer collaboration among learners and edu- cators. Blockchain-based platforms can provide secure environments for shar- ing educational resources, fostering collaboration, and incentivizing contributions through tokens or rewards. This decentralized approach promotes the creation and sharing of open educational resources, encouraging innovation and knowledge exchange. 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 17. 4 By leveraging the unique features of blockchain, educational institutions can over- come challenges related to data security, transparency, portability of records, and collaboration. However, it is important to carefully consider the implementation of blockchain in education and address technical, regulatory, and ethical consider- ations to maximize the potential benefits. 2.2 Potential Benefits of Implementing Blockchain in Education Implementing blockchain technology in education holds the potential to bring about several significant benefits. This section will explore some of the potential advan- tages that blockchain can offer to the field of education. Enhanced Data Security and Privacy: Blockchain ensures data security and pri- vacy by decentralizing and making it tamper-resistant, enhancing the protection of educational records and sensitive student information [3]. This can help protect against data breaches and ensure the integrity of academic records. Improved Verification and Credentialing: Blockchain revolutionizes credential verification by creating a tamper-proof repository of academic records, enabling easy and authentic verification for employers and institutions, reducing reliance on paper-based methods [14]. This streamlined and efficient verification process can help address issues related to credential fraud and enhance trust in the educa- tional system. Increased Transparency and Accountability: Blockchain’s transparency and auditability promote accountability in education by recording transactions and ensuring transparent processes, preventing fraud and fostering integrity in institu- tions [2].Additionally, the decentralized nature of blockchain can foster trust among stakeholders, as all participants have access to the same verified information. Facilitated Micropayments and Royalties: Blockchain technology enables the use of smart contracts, which are self-executing contracts with predefined rules and conditions. Smart contracts can facilitate micropayments and royalties for educa- tional content creators, such as authors, instructors, or developers of educational resources. Through blockchain-based platforms, creators can receive fair compen- sation for their work, fostering innovation and encouraging the production of high- quality educational materials [14]. Streamlined Administrative Processes: Blockchain has the potential to stream- line administrative processes in the education sector. By leveraging smart contracts, tasks such as student enrollment, course registration, and financial transactions can be automated and executed with increased efficiency. This can reduce administra- tive burdens, minimize errors, and free up valuable resources for educational institutions. Open and Collaborative Educational Ecosystem: Blockchain technology can facilitate the creation of an open and collaborative educational ecosystem. It can A. Marouan et al.
- 18. 5 enable the sharing and verification of educational resources, fostering collaboration among educators and learners. Blockchain-based platforms can provide secure environments for the creation, sharing, and adaptation of open educational resources, ensuring attribution, and incentivizing contributions [14]. 2.3 Examples of Universities and Educational Institutions Implementing Blockchain These examples showcase how universities around the world have started to inte- grate blockchain technology into their educational processes, ranging from issuing digital credentials to conducting research and offering specialized courses. The adoption of blockchain in education is still evolving, and more institutions are likely to explore its potential in the future: • MIT Media Lab (Massachusetts Institute of Technology): The MIT Media Lab has developed a blockchain-based platform called “Blockcerts” for issuing and verifying digital credentials. This platform allows students to store and share their academic achievements securely using blockchain technology. • Stanford University: Stanford has conducted research on using blockchain to secure and streamline academic transcripts. The university explored how block- chain can enhance data security and reduce administrative burdens related to transcript management. • University of Sydney: The University of Sydney has explored blockchain tech- nology to create a platform for students to store and share their academic creden- tials securely. This initiative aims to simplify the verification process for both students and employers. • King Abdullah University of Science and Technology (KAUST): KAUST in Saudi Arabia has partnered with IBM to explore the use of blockchain technol- ogy in various educational and research contexts. • Mohammed First University: Researchers in Moroccan universities are cur- rently working on utilizing blockchain technology in electronic voting. 3 Verifiable Digital Credentials 3.1 The Need for Secure and Verifiable Digital Credentials in Education In the digital era, the traditional methods of issuing and verifying educational cre- dentials are facing challenges in terms of security, portability, and efficiency. As a result, there is a growing need for secure and verifiable digital credentials in the 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 19. 6 field of education. Some of the reasons why secure and verifiable digital credentials are crucial in education and provide supporting references are mitigating credential fraud. Credential fraud, including the fabrication or alteration of academic achieve- ments, is a significant concern in the education sector. Traditional paper-based cre- dentials are susceptible to forgery and tampering, making it difficult to trust the authenticity of qualifications. Verifiable digital credentials, on the other hand, utilize cryptographic techniques to ensure the integrity and immutability of the credential data [14]. By implementing secure digital credentialing systems, educational insti- tutions can mitigate the risk of credential fraud and enhance trust in the qualifica- tions of their students. The second reason is enabling lifelong learning. In today’s rapidly evolving job market, lifelong learning has become essential for individuals to adapt and upskill. However, the recognition of informal and non-traditional learning experiences poses a challenge. Verifiable digital credentials can address this challenge by pro- viding a mechanism to capture and represent various forms of learning, including online courses, workshops, and work-based learning [3]. These digital credentials can be easily updated, stacked, and shared, enabling individuals to showcase their continuous learning journey. 3.2 Exploring the Use of Blockchain for Storing and Authenticating Credentials Blockchain technology has garnered significant attention in recent years, not only for its association with cryptocurrencies but also for its potential to revolutionize various industries. One area where blockchain shows great promise is in the storage and authentication of credentials. We use blockchain for the purposes on Fig. 1.2 and highlight its benefits and challenges. Fig. 1.2 The use of BCT in storage and authentication of credentials A. Marouan et al.
- 20. 7 4 Security and Privacy of Student Data 4.1 Current Challenges in Protecting Student Data In the digital age, educational institutions and organizations are increasingly relying on technology to manage and store student data. This shift has raised concerns about the security and privacy of student information. Safeguarding student data is of paramount importance to protect sensitive personal information and ensure trust within educational systems. Blockchain technology has emerged as a potential solu- tion to enhance the security and privacy of student data. Fig. 1.3 explores the key considerations and benefits associated with using blockchain in the context of stu- dent data security and privacy. 4.2 Leveraging Blockchain for Secure and Private Data Storage Blockchain technology has gained significant attention due to its potential for secure and transparent data management across various industries. One area where block- chain shows promise is in secure and private data storage. By utilizing its decentral- ized nature, immutability, and cryptographic algorithms, blockchain can provide robust solutions for protecting sensitive data from unauthorized access, tampering, and breaches. In this section, we will explore the key features of blockchain that make it suitable for secure and private data storage, as well as discuss some notable references in this field. Decentralization and Data Redundancy: Blockchain’s decentralized architecture eliminates the need for a central authority, such as a server or database, to store and manage data. Instead, data is distributed across a network of nodes, ensuring Data Integrity and Immutable Records Enhanced Data Security Blockchain Control and Ownership of Student Data Data Transparency and Auditability Blockchain’s immutability guarantees data integrity in education by creating tamper- proof(Mueller, A., 2018) and time- stamped records, making student data resistant to unauthorized modifications or deletions. secures student data through encryption and a decentralized architecture, mitigating the risk of unauthorized access and data breaches (Sack, C. and Davis, J., 2020), providing enhanced data security compared to centralized databases. Blockchain empowers students by giving them ownership and control over their data through decentralized identity managment systems(Hoffer et al., 2009), enhancing data privacy and mitigating the risks of misuse and unauthorized access. Blockchain’s transparency and auditable nature build trust in educational systems by allowing stakeholders to verify and audit student data(OECD, 2019), combating fraudulent credentials and ensuring the authenticity of student achievements. Fig. 1.3 Benefit of BCT for student data privacy 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 21. 8 redundancy and fault tolerance. This decentralized approach reduces the risk of a single point of failure, making it challenging for hackers to compromise the data. Additionally, storing multiple copies of data across the network enhances data avail- ability and integrity. Immutability and Data Integrity: Blockchain achieves data immutability through the use of cryptographic hashing algorithms [16] and consensus mechanisms. Each data block is cryptographically linked to the previous block, forming a chain of blocks. Once a block is added to the chain, it becomes computationally infeasible to alter or delete its contents without invalidating the entire chain. This feature ensures data integrity, as any unauthorized modification attempts can be easily detected. Encryption and Access Control: Blockchain technology can leverage advanced encryption techniques [7] to protect the confidentiality of stored data. By encrypting data before storing it on the blockchain, sensitive information remains secure even if the blockchain’s content is publicly accessible.Additionally, access control mech- anisms, such as public-private key pairs, can be implemented to grant authorized parties the ability to decrypt and access specific data. Smart Contracts and Data Management: Smart contracts are self-executing agreements with predefined rules and conditions stored on the blockchain. They provide an additional layer of security and automation for data storage and access. Smart contracts can enforce access controls, verify data integrity, and execute pre- defined actions based on specified conditions. By leveraging smart contracts, blockchain- based data storage systems can ensure secure and reliable data manage- ment [19, 23]. 5 Challenges and Limitations 5.1 Technical Barriers in Implementing Blockchain in Education While blockchain technology holds significant potential for transforming the educa- tion sector, there are several technical barriers that need to be addressed for success- ful implementation. These challenges can impact the scalability, interoperability, and integration of blockchain solutions within existing educational systems. One of the primary technical barriers is scalability. As blockchain networks grow in size and complexity, the computational and storage [10] requirements increase significantly. Public blockchains, such as Bitcoin and Ethereum, face scalability challenges in terms of transaction throughput and latency. This can pose a hindrance when it comes to processing a large volume of educational data, such as student records, certifications, and assessments. Several scalability solutions, including sharding and off-chain transactions, are being explored to address this challenge and improve the performance of blockchain networks. A. Marouan et al.
- 22. 9 Interoperability [20] is another technical hurdle in implementing blockchain in education. Educational institutions typically use a wide range of systems and plat- forms to manage student data, learning management systems, and other educational resources. Achieving seamless integration between blockchain and these existing systems is crucial to enable effective data exchange and interoperability. Efforts are underway to develop standardized protocols, such as the InterPlanetary File System (IPFS) and the W3C Verifiable Credentials, to facilitate interoperability between different blockchain networks and educational platforms. Security and privacy considerations also present technical challenges. While blockchain technology itself provides strong security through cryptography and immutability, securing the underlying infrastructure, such as storage, communica- tion channels, and user identities [13], is critical. Protecting sensitive student data, such as personally identifiable information (PII), from unauthorized access or data breaches requires robust security measures. Additionally, ensuring data privacy while leveraging the transparency and traceability of blockchain technology requires careful design and implementation. Furthermore, user experience and accessibility are important aspects to consider. The usability of blockchain applications and interfaces should be intuitive and user- friendly, ensuring that educators, students, and administrators can easily interact with the blockchain system. The technical complexity [12] associated with block- chain technology should be abstracted to provide a seamless user experience, enabling widespread adoption within educational settings. Addressing these technical barriers requires collaboration between educational institutions, technology providers, and blockchain experts. Research and develop- ment efforts are underway to create scalable blockchain solutions, enhance interop- erability, strengthen security measures, and improve user experience in educational blockchain applications. 5.2 Regulatory Considerations and Compliance Issues Implementing blockchain technology in the education sector requires careful atten- tion to regulatory considerations and compliance issues. As blockchain solutions involve the storage and management of sensitive student data, educational institu- tions must navigate legal and regulatory frameworks to ensure compliance with relevant laws and regulations. One significant regulatory consideration is data protection and privacy. Many jurisdictions have stringent data protection laws, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations impose strict requirements for the collection, stor- age, and processing of personal data, including student information. Educational institutions [5] adopting blockchain must ensure that their blockchain implementa- tions adhere to these regulations, providing appropriate consent mechanisms, data minimization, and secure data handling practices. 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 23. 10 In addition to data privacy, compliance with academic standards and accredita- tion requirements is essential. Educational institutions must ensure that blockchain- based systems for storing and verifying credentials meet the standards set by relevant accrediting bodies. This involves establishing trust in the blockchain sys- tem and ensuring the accuracy and integrity of the stored credentials. Collaborating with accrediting bodies and regulatory agencies can help ensure that blockchain implementations in education comply with the necessary standards and regula- tions [6]. Furthermore, intellectual property rights and copyright considerations should not be overlooked. Blockchain technology [11] enables the transparent sharing and dis- tribution of educational content, raising questions about ownership and licensing rights. Educational institutions [15] must navigate copyright laws and establish clear guidelines regarding the use and distribution of educational materials on the blockchain. Collaborations with content creators, licensing agencies, and legal experts can help address these regulatory challenges effectively. It is important to recognize that regulatory frameworks surrounding blockchain technology in education are still evolving. As such, educational institutions should actively monitor updates and engage in discussions with policymakers and regula- tory authorities to shape the regulatory landscape and ensure compliance with emerging requirements. 5.3 Scalability Challenges and Potential Solutions Scalability is a significant challenge when implementing blockchain technology, as it involves handling a large volume of transactions and data within a network. This challenge becomes particularly crucial in sectors like education, where the storage and processing of extensive student records, certificates, and assessments are involved. Addressing scalability concerns is crucial to ensure that blockchain-based educational systems can handle increased transactional demands and accommodate growing user bases [1]. One of the primary scalability challenges in blockchain technology is transaction throughput. Public blockchains, such as Bitcoin and Ethereum, have limitations in terms of the number of transactions they can process per second. For instance, Bitcoin can handle around 7 transactions per second, while Ethereum’s throughput [9] is higher but still limited. This limitation can result in delays and increased trans- action fees, impeding the seamless flow of data and transactions within an educa- tional blockchain ecosystem. To overcome these challenges, several potential solutions are being explored. One approach is the implementation of off-chain transactions or layer-2 scaling solutions. These solutions involve conducting certain transactions or computations off the main blockchain, reducing the load on the main network. Off-chain transac- tions can be settled periodically on the main blockchain, maintaining the security and immutability of the underlying data. A. Marouan et al.
- 24. 11 Another solution is the concept of sharding, which involves dividing the block- chain network into smaller, interconnected sub-networks known as shards. Sharding allows for parallel processing of transactions and data across multiple shards, sig- nificantly increasing the overall capacity and transaction throughput of the block- chain network. By distributing the workload across shards, scalability can be improved while maintaining the security and decentralization aspects of the blockchain. Additionally, advancements in consensus algorithms offer potential scalability improvements. Traditional proof-of-work algorithms, while secure, are computa- tionally intensive and limit scalability. Alternative consensus mechanisms [18] like proof-of-stake, proof-of-authority, or delegated proof-of-stake can provide higher transaction throughput and lower energy consumption, enabling greater scalability for blockchain networks. Furthermore, advancements in infrastructure, such as high-performance hard- ware and optimized software, can contribute to improved scalability. Increasing net- work bandwidth, storage capabilities, and computational power can help handle larger volumes of data and transactions within a blockchain system. It is important to note that scalability solutions for blockchain technology are still evolving, and their effectiveness and applicability may vary depending on the specific use case and requirements of educational blockchain implementations. Ongoingresearchanddevelopmenteffortscontinuetoexploreinnovativeapproaches to address scalability challenges in blockchain technology. 6 Future Research and Experimentation 6.1 Importance of Continued Research and Experimentation Continued research and experimentation play a vital role in unlocking the full potential of blockchain technology in the education sector. As blockchain is a rela- tively new and evolving technology, there are still many aspects to explore, refine, and optimize. Investing in research and experimentation allows educational institu- tions to stay at the forefront of innovation and harness the transformative power of blockchain in education. One key importance of continued research is to address technical challenges and limitations associated with blockchain implementation. Researchers can focus on scalability, interoperability, security, and privacy issues to develop solutions that overcome these hurdles. Through experimentation, researchers can test new consen- sus algorithms, explore novel approaches to data storage and management, and pro- pose frameworks for secure and efficient integration of blockchain with existing educational systems. Moreover, research efforts can help identify and understand the potential benefits and impacts of blockchain technology in education. Studies can evaluate the 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 25. 12 effectiveness of blockchain-based solutions in improving processes such as creden- tial verification, lifelong learning tracking, and decentralized educational content sharing. By examining real-world use cases and conducting empirical studies, researchers can provide insights into the value proposition of blockchain and guide its adoption in education. Continued research also facilitates collaboration among academia, industry, and policymakers. By fostering interdisciplinary dialogue, researchers can contribute to the development of regulatory frameworks, standards, and best practices for block- chain implementation in education. This collaboration ensures that blockchain solu- tions align with legal and ethical considerations while addressing the specific needs and challenges of educational institutions. Furthermore, research and experimentation can drive innovation in the design and implementation of user-friendly blockchain interfaces and educational applica- tions. By focusing on user experience, researchers can make blockchain technology more accessible and intuitive for educators, students, and administrators. This includes simplifying complex concepts, improving the usability of blockchain inter- faces, and designing intuitive educational platforms that leverage blockchain’s benefits. In summary, continued research and experimentation are essential for advancing the adoption and effectiveness of blockchain technology in education. It enables the development of scalable, secure, and user-friendly solutions; informs policy and standards; and explores the full potential of blockchain in transforming educational systems. 6.2 Exploring and Refining the Potential of Blockchain Technology in Education Blockchain technology has gained significant attention in various industries due to its potential to enhance transparency, security, and efficiency. In recent years, the education sector has also recognized the value of blockchain in revolutionizing tra- ditional processes and transforming the way educational credentials are managed and verified. This section explores the potential applications of blockchain technol- ogy in education and highlights the need for further refinement and exploration. One of the key areas where blockchain (L. Uden, M. Sinclair, Y.-H. Tao, D. Liberona, R. M. A. Pinto (Eds.) Anderson, S., 2016) can make a significant impact is in the verification and authentication of educational certificates and cre- dentials. With the current system heavily reliant on paper-based records and manual verification processes, the potential for fraud and misrepresentation is high. Blockchain technology can offer a decentralized and immutable ledger, ensuring the authenticity of educational records. Employers and academic institutions can easily verify the credentials of applicants, saving time and resources. Moreover, blockchain-based systems can facilitate the secure transfer and stor- age of academic records, enabling students to have complete ownership and control A. Marouan et al.
- 26. 13 over their educational data. This empowers learners to share their achievements with potential employers, institutions, or other stakeholders, eliminating the need for intermediaries and enhancing data privacy. Furthermore, blockchain-based microcredentialing systems have the potential to revolutionize lifelong learning and skills development. These systems can provide learners with the ability to earn and store digital badges or tokens for completing specific courses or acquiring specific skills. These digital credentials can be easily verified and shared, enabling employers and educational institutions to assess an individual’s competency and skill set accurately [8]. Despite the promising potential of blockchain in education, further exploration and refinement are necessary to address challenges and ensure successful imple- mentation. Issues such as scalability, interoperability, and standardization need to be carefully considered. Scalability challenges arise due to the decentralized nature of blockchain networks, as they require significant computational power and stor- age capacity. Innovative solutions such as off-chain scaling techniques and layer- two protocols can help overcome these challenges. Moreover, regulatory considerations and compliance issues play a crucial role in the adoption of blockchain technology in education. Compliance with data protec- tion and privacy laws, as well as addressing concerns related to data ownership and control, is essential. Collaboration between educational institutions, policymakers, and blockchain technology providers is necessary to establish a robust regulatory framework that ensures data security and privacy while promoting innovation. 7 Conclusion This chapter has highlighted the key points and findings regarding the potential of blockchain technology in education. It has emphasized the benefits of blockchain in verifying credentials, enabling secure record-keeping, and revolutionizing micro- credentialing. However, challenges such as scalability need to be addressed for suc- cessful implementation. Overall, blockchain has the potential to transform education by enhancing transparency, security, and learner empowerment. In summary, the implementation of blockchain in education presents various implications and challenges. While it holds promise for verifying credentials, ensur- ing data security, and enabling lifelong learning, scalability and regulatory consid- erations need to be carefully addressed. Overcoming these challenges will be crucial to fully harnessing the potential benefits of blockchain technology in education. In final thoughts, blockchain technology has the transformative potential to revo- lutionize education. Its ability to enhance transparency, security, and ownership of educational records can empower learners and streamline processes. However, suc- cessful implementation requires addressing scalability challenges, regulatory con- siderations, and fostering collaboration among stakeholders.With careful refinement and exploration, blockchain has the capacity to reshape education, making it more accessible, efficient, and learner-centric. 1 Empowering Education: Leveraging Blockchain for Secure Credentials and Lifelong…
- 27. 14 References 1. Al-Bahri, M., Al-Bahri, A., Al-Khalifa, H., Al-Hassan. (2019). Survey and evaluation of scalability of blockchain consensus algorithms. In 2019 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–6. 2. Al-Busaidi, A. A., Mayhew, L. (s.d.). Blockchain technology: Implementation challenges in global supply chains. 157, 120079. 3. Ali, R., Khan, W. A., Bashir, A. K. (s.d.). Blockchain-based decentralized system for reli- able student data management. 142, 103647. 4. Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking digital cryptocurrencies. O’Reilly Media, Inc. 5. Azzi, N., Jarju, B. S. (s.d.). Privacy preserving blockchain-based certificate verification: a review. 94–107. 6. Bartolucci, F., Donnelly, K. (2019). Blockchain in education: Examining potential, pit- falls, and perspectives. In Proceedings of the 52nd Hawaii international conference on system sciences. 7. Benet, J. (2014). Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561. 8. Choudhury, S. R., Abraham, A. (s.d.). A blockchain future for higher education? In Proceedings of the 2018 international conference on data science and computational intel- ligence, pp. 44–49. 9. Croman, K., Decker, C., Eyal, I., Gencer, A. E., Juels, A., Kosba, A., … Song, D. (2016). On scaling decentralized blockchains. In International conference on financial cryptography and data security, pp. 106–125. 10. Gao, F., Xu, X. (s.d.). Privacy preservation in blockchain: Research challenges and oppor- tunities. 22218–22236. 11. Griggs, K., Jackson, E. (s.d.). Blockchain in education. EdTech, 15(5), 10–14. 12. Gupta, R. K., Patel, N., Nandini, K. (2021). Blockchain and data protection regulations: Challenges, opportunities and future prospects. Computers Security, 108. 13. Janssen, B., Debruyne, C. (2020). Blockchain in education: Risks and opportunities. Frontiers in Blockchain, 3–15. 14. Khan, I. A., Iqbal, W. (s.d.). Blockchain technology in education: Opportunities, challenges, and solutions. IEEE Access, 9, 107211–107230. 15. Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. 16. Uden, L., Sinclair, M., Tao,Y. -H., Liberona, D., Pinto, R. M. A., Anderson, S. (Eds.) (2016). Blockchain technology and education. Learning Technology for Education in Cloud—The Changing Face of Education : 5th International Workshop, LTEC 2016 (Vol. 738, p. 214–222). https://doi.org/10.1007/978-3-319-71940-5_21 17. Middleton, H., Urquhart, L. (s.d.). Blockchain in education. International Journal of Educational Technology in Higher Education, 15, 1–24. 18. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review, 21260. 19. Oosterhof, H., Snijders, N. (s.d.). GDPR and blockchain: Friends or foes? International Data Privacy Law, 10(3), 227–246. 20. Swan, M. (2015). Blockchain: Blueprint for a new economy. O’Reilly Media, Inc. 21. Tapscott, D., Tapscott, A. (2016). Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world. Penguin. 22. Tschorsch, F., Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decen- tralized digital currencies. IEEE Communications Surveys Tutorials, 18(3), 2084–2123. 23. Wilkinson, S., Boshevski, T., Brandoff, J., Buterin, V. (2014). Storj a peer-to-peer cloud storage network. Citeseer. 24. Yin, J., Qin, Z., Wen, Q. (2020). Blockchain technology in education: Recent advances and future prospects. Computers Education, 103723, 145. 25. Yli-Huumo, J., Ko, D., Choi, S., Park, S., Smolander, K. (2016). Where is current research on blockchain technology?—A systematic review. PLoS One, 11(10), e0163477. A. Marouan et al.
- 28. 15 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 S. M. Idrees, M. Nowostawski (eds.), Blockchain Transformations, Signals and Communication Technology, https://doi.org/10.1007/978-3-031-49593-9_2 Chapter 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare Security Pranay Shah, Sushruta Mishra, and Angelia Melani Adrian 1 Introduction The healthcare industry is in dire need of reform, from infectious diseases to cancer to radiography. There are various ways to use technology to deliver more precise, trustworthy, and efficient solutions. Artificial intelligence is the technology used to carry out works that would typically need human insights. Machine learning is a branch of AI that enables us to improve the AI algorithm by utilizing large amounts of data collected dynamically. AI is capable of comprehending and interpreting lan- guage, analyzing audio, recognizing things, and finding patterns to perform various kinds of tasks. This chapter demonstrates different perspectives of artificial intelli- gence in audio, video, and text and the challenges it faces in healthcare. Artificial intelligence includes natural language processing (NLP), machine learning for healthcare imaging, and acoustic AI. Natural language processing assists to enable computers to grasp texts and languages like that of humans. Once completed, com- puter systems interpret, sum up, and synthesize precise text and language from the given data. The healthcare sector produces a considerable quantity of written infor- mation, such as clinical reports, lab results, handwritten notes, admission and dis- charge records, and others, as illustrated in Fig. 2.1. Interpreting and managing such an enormous volume of data manually would be extremely challenging for health- care professionals. The three main tasks that can be driven by NLP are opinion mining, information classification, and extracting significant facts from the text. NLP assists by analyzing and transforming these expanding data sets into a P. Shah · S. Mishra (*) Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India e-mail: sushruta.mishrafcs@kiit.ac.in A. M. Adrian Universitas Katolik De La Salle Manado, Manado, Indonesia
- 29. 16 Fig. 2.1 Illustration of machine intelligence in healthcare computer-manageable format. Additionally, it can support professional judgment; pinpoint vulnerable patients; and categorize diseases, syndromes, symptoms, and disorders. Similarly, machine learning for healthcare imaging is done through com- puter vision. Computer vision focuses on teaching computer systems to imitate human sight and analyze and interpret the things around them. Using artificial intel- ligence algorithms that evaluate images, computer vision does this. Medical records such as X-ray reports, CT scan, MRI scan, images of ultrasound, and videos play a vital role in a patient’s diagnosis. Computer vision can improve the monitoring of patients, diagnose automatically, and generate lab reports automatically through various techniques such as object identification, categorization, location, and analy- sis from images or videos. It can encourage the development of a variety of applica- tions that could save patients’ lives in the fields of dermatology, oncology, cardiology, radiology, and fundoscopy. Similarly, as shown in the audio part of Fig. 2.1, differ- ent patterns of sounds of breathing, beating of heart, wheezing, crying, coughing, and so on, have a significant part in the diagnosis of various respiratory, pulmonary, and cardiac-related diseases. By examining noises, categorizing them, and evaluat- ing them along the spectrum of audio, AI helps automate these diagnoses. Modern machine learning algorithms are available for processing audio signals, useful in the healthcare sector. For AI models to be more widely used in the healthcare sector, they must overcome several standards and problems, as shown in Fig. 2.1. First, when trained on sufficiently enough datasets, AI models can function precisely. Hence, one of the main difficulties is finding large, reliable, and trustworthy datas- ets for training. Second, it might be feasible if we chose to compile information from several sources and protect data from confidentiality violations and security P. Shah et al.
- 30. 17 threats. Third, because AI models are inherently opaque, it can be challenging to spot biased algorithms. In order to address the issue of mistrust in the learned model, it is necessary to have a record of the prediction or classification that arises from a particular healthcare input. If the incorrect course of action is taken based on AI conclusions, human lives are at risk. Fourth, there should be safe resource sharing to combat the threat posed by rogue devices. Finally, difficulties with information privacy exist when researchers and clinicians share their knowledge. As a result, there needs to be a tested plan to get through these obstacles beforeAI takes over the healthcare sector. Learning and exploring through data increases the awareness and efficiency of AI-based healthcare in terms of accurate diagnosis and treatment planning while posing issues with anonymity, data governance, and the ability to generate income from sensitive patient data. So, it is necessary to make certain that the quality of the final therapy is good and that the patients quickly get the treatment required to man- age their acute or chronic illnesses. The use of AI and ML can significantly enhance healthcare. However, the potential for adversarial attacks on natural language pro- cessing, AI healthcare imaging, and acoustic AI poses a constraint on their wide- spread use. In highly sensitive application domains like healthcare, these assaults cannot be tolerated. Considering the threats in these fields, blockchain can defend against these adversarial assaults. The intersection of blockchain technology and healthcare based on AI has the capability to completely change security and privacy. This review shows the possible use of AI-based blockchain integration in the sector of healthcare. In order to strengthen AI-based healthcare systems, this research relates the security measures using blockchain for the adversarial threats in NLP, healthcare imaging using ML (computer vision), and acoustic AI. The main contributions of this chapter are as follows: • A brief introduction to existingAI technologies in healthcare is discussed and the role of blockchain in clinical security is highlighted. • A succinct overview of the existing advanced security based technologies used in the healthcare system is discussed. • Various security threats, and adversarial attacks on AI technologies that are used in healthcare (NLP, medical imaging using machine learning, acoustic AI) are presented along with modern blockchain solutions. 2 Outline of Advanced Technologies Used in Healthcare Advanced technologies have been integrated into the healthcare system nowadays to assist with patient monitoring, diagnosis, treatment, research, decision-making, hospital management, and so on. Some of these are the Internet of Things, block- chain, cloud computing, and artificial intelligence. They also provide automation, intelligence, security, and a low-cost computational ecology, which forms the base 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 31. 18 and enhances the functionality of the established healthcare system. The key enabling technologies and their main attributes used are shown in Fig. 2.2. Also, Table 2.1 displays a comparison of the numerous services provided by contempo- rary technologies including blockchain, IoT, AI, and cloud computing. Fig. 2.2 Technologies used in advanced healthcare systems and their features Table 2.1 Comparison of different services provided by advanced technologies Modern technologies Advantages Challenges Blockchain Security, immutable, decentralized, Trustable, transparent Requires more bandwidth, relatively expensive and complex than existing databases Artificial intelligence Compatible with different platforms. Versatile, reliable, efficient Complex and difficult to design, expensive and difficult for deployment Cloud computing Efficiency, more capacity, ease of data storage, flexible Difficult to manage, security threats, privacy concerns, high cost of communication Internet of things Low latency, portable, availability, efficient algorithm Poor computation, privacy concerns, traffic P. Shah et al.
- 32. 19 3 Blockchain-Enabled Technology in Healthcare Blockchain technology was initially created to provide support for the use of cryp- tocurrency. However, recently it has been applied to different fields, achieving exceptional security [1]. At present, the healthcare industry has begun to integrate blockchain into several aspects of its operations. Its attributes, such as decentralized exchanges, micro-transactions, smart contracts, and consensus mechanisms, can help safeguard the confidentiality of patient data, which is an important asset of the healthcare sector. A blockchain is a type of immutable ledger that is distributed and replicates transactions across its network, incorporating cryptographic links in chronological order between information. It consists of consensus protocol and smart contracts to ensure security. It can overcome the obstacles experienced by AI by establishing trust among users, organizing data, and facilitating resource sharing in AI-based healthcare. Peer-to-peer networks and decentralized ledger is a feature of block- chain technology. Transactional records are securely maintained by a distributed ledger. By logging local gradients on the blockchain, this functionality supports the safe learning of heterogeneous data. Similarly, the execution of transactions in a distributed network without the involvement of a third party or centralized authority is done automatically by a smart contract. It is a piece of executable code that is present on every node and that is triggered when a transaction is initiated. The trans- action is validated via smart contracts. Smart contracts enable the imposition of access control regulations for data access. Smart contracts enable user provenance. In the case of transactional data, a block is produced. A consensus algorithm is used by miners to commit the block to the blockchain. Algorithms of consensus mine the block. It forces miners to work through challenging cryptographic riddles and pub- lish their solutions with other miners. The opportunity to mine a block of the trans- actions and adding it to the available chain and duplicating the created chain in all the connected nodes is given to the miner who solves the challenge first. Consensus algorithms may be the effective method for group decisions on diagnosis and treat- ment in AI-based healthcare systems. Blocks are immutable and auditable since they are cryptographically connected to one another. When the transaction is copied and duplicated across all network nodes, the highest level of availability and trans- parency is achieved. Medical data can be verified via cryptographic linking, which can also provide a tamper-proof duplicate of it. Anyone can join the network and take part in transactions while using a public blockchain. Private blockchain, on the other hand, places restrictions on access without sufficient authentication and veri- fication. Public and private blockchain characteristics are combined in consortium blockchain. The working of the blockchain is shown in Fig. 2.3. Figure 2.4 shows some of the applications of blockchain in healthcare. Securing patient’s medical data and effectively managing various product supply chains, including those for medical equipment, organs, medicines, drugs components, oxy- gen cylinders inclusive of all other pharmaceuticals, are two essential criteria of the healthcare business. 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 33. 20 Fig. 2.3 Process workflow involved in blockchain Fig. 2.4 Blockchain and healthcare Blockchain is a leading-edge technology that has the potential to revolutionize the healthcare system by providing security, dependability, confidentiality, and compatibility [2]. It features an unalterable and distributed ledger in which patients’ medical data can be stored securely and prevented from tampering. It is safeguarded P. Shah et al.
- 34. 21 by cryptographic elements like hashing, digital signatures, and asymmetric keys ensuring that the data cannot be tampered with [3]. Since the ledger is decentralized, any slight alteration to a data transaction will be detected by all blockchain mem- bers, resulting in greater transparency across the entire system. Using blockchain technology enables safe medical data transmission, prevents breaches, and effective management of medical resources as the healthcare sector is constantly at risk of being attacked. A healthcare system that uses blockchain technology was presented by the authors of [4] to protect the confidentiality of user data. They also utilized various mechanisms to safeguard users’ confidential information, developed smart contracts in order to authenticate transactions of data, and provide control access and decision-making in an open network. A safe and dependable blockchain- adapted strategy to prevent security violations of electronic medical record systems was put forth by Ray et al. in [5]. To enable secure data sharing over the IoT net- work, they deployed private blockchain and swarm intelligence techniques. Moreover, Subramanian et al. [6] examined the use of blockchain and AI technol- ogy in the treatment of diabetes disorders, particularly during the COVID-19 pan- demic. Similarly, medical facilities, testing facilities, academic institutions, and patients may share useful information and collaborate to enhance the AI model. Nevertheless, due to privacy and security issues, they have trouble sharing crucial data with outside parties. Hence, a barrier to raising the caliber of AI-based health- care systems is secure data sharing. In order to improve the prediction of lung can- cer using CT scan pictures, Kumar et al. [7] suggested a method that involves exchanging regional models through the network of blockchain. Hence, the updated model assists in precisely diagnosing the ailment of the patients, leading to enhanced therapy. By preventing actual data sharing, privacy is maintained. Organizations will exchange local gradients via smart contracts and transfer their data to the IPFS (Interplanetary File System). The global model is trained using a consensus approach called Delegated Proof-of-Stake. A smart contract establishes trust in the data, and the blockchain is updated with the local gradient’s hash. To expedite bio- medical research, Mamoshina et al. [8] have offered AI and blockchain technolo- gies. It also provides patient incentives to get regular examinations and benefits from new technology for managing and making money through their personal infor- mation. Patients can sell their medical records using tokens on the permissioned blockchain platform called Exonum, which has been proposed by the group. Nevertheless, once data has been sold to authorities, this framework has no control over it. An intrusion detection system has been proposed by Nguyen et al. [9] to safeguard data transfer in the healthcare industry’s cyber-physical system. Patients frequently lack control over who has access to their medical data. A safe, immuta- ble, and decentralized gradient mining is used in place of the insecure central gradi- ent aggregator on the blockchain. Smart contracts are used to control the edge computing, management of trust, authentication, and distribution of trained models, as well as the identification of nodes and the datasets or models used by them. This method offers total encryption for both a trained model and a dataset. A decentral- ized AI-powered healthcare system has been built by Puri et al. [10] that can access 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 35. 22 and verify IoT devices along with fostering trust and transparency in the health records of patients. This method uses the creation of a public blockchain network and AI-enabled smart contracts. The framework also finds IoT nodes in the network that might be harmful. BITS is a special intelligent TS system built on blockchain that is offered by Gupta et al. [11]. They offer extensive insights into the blockchain- and cloud-based smart T’s frameworks, highlighting the difficulties with data man- agement, security, dependability, and secrecy. To maintain the security and privacy of the IoMT, Polap et al. [12] have provided distributed learning. It utilizes decentralized learning along with blockchain secu- rity enabling the creation of intelligent systems that preserves confidentiality by keeping the data locally stored. The model poisoning attacks can be lessened by using this approach. Kumar et al. [13] described a method to detect people infected with COVID-19 through CT images by developing a model jointly using blockchain technology and federated learning to maintain secrecy. To solve these challenges, Chained Distributed Machine Learning (C-DistriM), which is a unique decentral- ized learning that also uses blockchain-based architecture, has been predicted by Zerka et al. [14] to be made for imaging in the medical field. Blockchain preserves model integrity and records the unchangeable history of computation. The Explorer Chain framework, which was proposed by Kuo et al. [15], aims to build a model that can predict throughout the distributed architecture. The framework employs machine learning and blockchain technology that does not require patient data shar- ing or a central coordinating node, making it decentralized and without a central authority. Similarly, in order to establish the transmission of data, transfer of the model, and its testing in three places in China and Singapore, Schmetterer et al. [16] implemented a blockchain-enabled AI technology. A wireless capsule endoscopy approach for identifying stomach infections was investigated by Khan et al. [17]. A complex artificial neural network model is secured using a blockchain-based method to enable accurate diagnosis of gastrointestinal conditions like tumors and bleeding. Each part includes a separate block that stores specific data to fend off attempts that would temper or modify it. Natural language processing (NLP) technology, in par- ticular, has proven an efficient tool to categorize the emotion, and feelings of texts, present in social media such as posts, according to Pilozzi et al. [18]. These methods could be applied to learn more about how people see Alzheimer’s disease. Patients will have more control over their data if decentralized, secure data transit and stor- age techniques like blockchain are used. Most of the anxieties associated with mis- takenly revealing personal information to an organization that might treat the patient unfairly will be eliminated. The work that has been done in blockchain for AI-based healthcare is shown in Fig. 2.5. It shows the kind of blockchain that is used for the various data modalities. Blockchain is categorized into three types: consortium, pri- vate, and public blockchain. The most used public blockchain is Ethereum and the private is Hyperledger. P. Shah et al.
- 36. 23 Public Blockchain Consortium Blockchain Private Blockchain Not mentioned General Data Image Data Text Data Audio Data M o m o s h i n a e t a l . 2 0 1 8 Polap et al. 2020 Zerka et al. 2020 Rahman et al. 2020 Pilozzi et al. 2020 K i m e t a l . 2 0 2 0 G up ta et al . 20 20 K ha n et al . 20 21 P u r i e t a l . 2 0 2 1 Nguyen et al. 2021 Kumar et al. 2021 Schmetterer et al. 2021 Kumar et al. 2021 K u o e t a l . 2 0 2 0 J e n n a t h e t a l. 2 0 2 0 Fig. 2.5 Types of blockchain, data 4 Role of Artificial Intelligence in Smart Healthcare Systems The healthcare sector demands advanced and anticipatory solutions that offer boundless prospects for accurate and beneficial patient treatment and management operations [19]. IoT technologies generate a vague volume of data and transfer it to and from different parts of the health industry. The health industry needs to imple- ment AI technologies for the effective management of data and its improvisation. The employment of different AI devices in the health sector has many benefits over the current system, which relies on time-consuming data analysis and decision- making methods. In order to offer insightful information about diagnosis, clinical decision support, and treatment, it interacts with medical data. For instance, in [20], scientists looked at the osteoporosis condition, which is typically identified by conventional X-rays and MRI scans. An AI-featured selec- tion technique was used by the authors to facilitate the diagnosis of osteoporosis patients through the data obtained by ultrasound. As a result, they were able to clas- sify osteoporosis patients’ fracture risk with 71% accuracy. Wazid et al. [21] dis- cussed the key features ofAI technologies in the healthcare industry. They employed AI algorithms to effectively forecast the likelihood of myocardial infarction and the 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 37. 24 possibility of developing tumors while also uncovering insightful patterns in the medical data. In [22], Parra et al. investigated how AI algorithms could be used for sustainable development. Here, they looked at the people who required an AI-based question-recommendation system for various scenarios. The main goal of their study was to support the suggestion for AI-based questions in the health industry, having the potential to be used in a vast number of applications beyond security screening and financial services. Similarly, Tedeschini et al. [23] used federated learning, a decentralized technique, to create a distributed networking architecture for segmenting brain tumors based on message queuing telemetry transport (MQTT). Their findings demonstrate that the suggested framework performs more accurately and quickly during routine healthcare system activities. A data processing method called machine learning automatizes the creation of models capable of analysis. It focuses on enabling computers to study data, find patterns, and make human-like choices without actual human input. In order to complete these tasks, validated data had to be obtained. After the classifiers were successfully trained, the model had to be deployed. Retraining and feedback loops may be used to continue improving performance. Any attempt to uncover, alter, dis- able, harm, capture, or collect information by taking advantage of system weak- nesses constitutes a threat to the device. The fundamental security requirement for any system is to maintain the privacy of sensitive data or processes. In order to keep the trained model from malfunctioning, three vitals must also be safeguarded. The section that comes after Fig. 2.6 focuses on the AI attack surface. Any adver- sary can attack an AI-based system by targeting data, classifiers/algorithms, and learning models. Attack Data Adversarial Attack Spoofing Physical Attack Trojan/Backdoor Attack Timing Side Channel Attack Classifier/ Algorithm Model Fig. 2.6 Attack surface of artificial intelligence P. Shah et al.
- 38. 25 4.1 Data Data is the raw statistics and facts used by a machine. It is a critical component of artificial intelligence. Data is required to train current models and development of all current technologies. It costs a lot of money just to get as much precise data as you can. The attack’s data-targeting strategy has a significant impact on AI-based systems. By taking advantage of the extraordinary sensitivity of AI to detect slight differences in the input, known as a poisoning attack, data can be violated either during the learning phase or during the filed-test. These assaults may be promoted via spoofing [24]. It is a type of cyberattack when a malicious party uses a computer, device, or network to pretend to be someone else in order to trick other computer networks. Malicious opponents typically cannot access the training phase of the model. In order to trick a classifier or avoid being detected by a neural network dur- ing testing, they produce hostile input. These attacks can be of the physical or digital variety. For this study, we are concentrating on cyberattacks of various kinds. A digital technique immediately introduces small input perturbations. In this case, the attacker can take advantage of the system that has been targeted without the detec- tion system noticing. Concept drift might also result from evasion attacks [25]. Prospective attackers may potentially acquire access to the training datasets and conduct poisoning attacks, which contaminate the datasets with adversarial sam- ples. As will be covered in more detail in subsequent parts, adversarial attacks can cause potential damage to the system. 4.2 Classifier/Algorithm Classifiers/Algorithms are usually affected by a Back-access Attack. A Trojan assault undermines the authentic model by incorporating a secret entrance to the neural network, which is triggered by a specific pattern in the testing data. This will alter the network using a compromised dataset [26]. Trojan assaults vary from adversarial attacks even though both only occur during the training phase. An adversarial attack merely influences the outcome in this scenario rather than forc- ing the neural network to change itself. But, a trojan assault, causes the network to change itself because of the poisoned input samples so that it can accurately func- tion for benign input samples. As a result, the network will only malfunction when a trojan causes it to. A user may have trouble recognizing the trojan assault [27]. A trojan attack may result from a SPA (Stealthy Poisoning Attack), which is depen- dent on a Generalized Adversary Network (GAN) [28]. Another illustration of a neural trojan assault is Badnet [29]. The situation of a trojan assault is shown in Fig. 2.7. 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 39. 26 Fig. 2.7 A sample example of a trojan attack 4.3 Model The model is affected by timing side-channel attack. Neural networks are suscep- tible to attacks like timing side channels because of their peculiar qualities of hav- ing varied execution times depending on the depth of the network. An opponent can determine the layers of the neural network, by observing how long the model takes to generate output. This adversary makes use of a regressor that was trained using various network layer counts and execution timings. The information is then used to create replica models that have features in common with the original net- work [30]. The essential elements are retained due to memory access patterns. Reverse engineering of the CNN model’s structure and weights can be used to leak information through memory and timing side-channel attacks. The crucial charac- teristics of a neural network [31], such as the overall layers, the size of each layer, and the interdependencies among them, are exploded by the memory access patterns. P. Shah et al.
- 40. 27 5 Machine Intelligence Technologies in Healthcare 5.1 Text-Based AI Technology The text-based AI technology widely used is natural language processing. It is a machine learning model, which aims to enable computers to understand, process, and generate human-like text and language. The goal of NLP is to build systems and algorithms that can perform a variety of tasks involving human languages, such as text classification, machine translation, sentiment analysis, recognition of speech, and natural language generation. 5.1.1 Applications in Healthcare Clinical Decision Support Systems (CDDS) receive a variety of inputs, including incomplete structured data like XML files, structured data like HER, and unstruc- tured data like diagnostic summaries and progress records. To aid clinical decisions, various systems have been introduced that utilize NLP techniques that take input from unstructured data, specifically for the purpose of calculating and automating diagnoses or treatments. With the help of NLP, CDSS can create results and recom- mendations that help healthcare practitioners make the best decisions possible by automatically extracting key information from free text [32, 33]. NLP makes it easy to extract important clinical information from unstructured data in medical records, such as physician notes, discharge summaries, and diagnostic reports. This can help with coding, billing, and clinical decision-making. A sentiment score system has been used to assess sentiment statements of admission and discharge in a hospital [34]. Unstructured reports are also used for radiology. NLP enables the recognition of key aspects in those reports, their extraction, and conversion into usable com- puter formats [35]. The analysis of vast amounts of free-text medical reports using NLP contains the potential to contribute to the development of procedure-intensive fields such as Hepatology. Also, NLP can be used to develop chatbots and virtual assistants that can answer patient questions and provide basic medical advice. This can help patients access healthcare information quickly and easily [36]. In a recent study, NLP was used to categorize diseases and conditions which were challenging to identify through simple clinical procedures. Using NLP-based solutions for information retrieval (IR) reduces the time and effort required, ultimately promot- ing the therapy [37, 38]. 5.1.2 Adversarial and Defense Attack An alteration in the text’s semantics, grammar, or visual similarity that deceives NLP is known as adversarial text. The techniques for creating hostile text are shown in Fig. 2.8. To impact the model’s prediction, text-based adversarial examples can 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 41. 28 Fig. 2.8 Taxonomy of adversarial attack on text Adversarial Attack in healthcare application NLP Normal Text Risk:High Adversarial Attack Enlargement of the Heart Shortness of Breath Irregular heartbeat Adversarial Text Risk:Low Expansion of the heart Shortness of Respiration Non-Uniform Heartbeat Fig. 2.9 Adversarial attack on NLP-based healthcare application be created by making slight changes to the text, mimicking real typing errors made by humans. These modifications are intended to be minimal yet effective in altering the model’s output. After the attack, the text appears fairly near to the original. This method of attack is used in assaults like hot flip [38], textbugger [39], and DeepWordBug [40]. The process of making hostile writing involves paraphrasing the source material. The semantic equivalent of the original text will be produced by this assault, but the model’s result for the original text and the modified text will be different. Figure 2.9 shows how NLP-based healthcare is affected by adversarial attacks. The NLP can be tricked by simply substituting synonyms for words while keeping the text’s semantics. The incorrect diagnosis ultimately results in the incorrect treat- ment, endangering lives. P. Shah et al.
- 42. 29 5.1.3 Blockchain-Based Solution for NLP-Based Healthcare As mentioned above, NLP faces different types of adversarial attacks. Contrarily, those attacks are only somewhat sophisticated; in the case of text data, these attacks are plainly visible to the naked eye. So, we might draw the conclusion that there is a moderate chance that such attacks will occur. In order to address attack surfaces including data, classifiers, and models in NLP, we have framed blockchain solutions. Data Layer In NLP, data may be stored locally on the computers of data owners, such as physi- cians, hospital staff, and laboratories database. A peer-to-peer blockchain network can be created with dispersed owners to look into the issue of having enough data for training AI models while also maintaining the secrecy of the data by enabling owners to transfer their data indirectly with other parties. Off-chain data storage is supported by this framework. This peer-to-peer network provides direct service exchange using a suitable authentication method. Without a centralized server, thousands of devices can be linked together. The P2P blockchain node can take on the role of a service provider or requester. Rules can be inferred for access control to allow private data sharing through smart contracts. To verify the integrity of dis- tributed data, a hash code is generated, which is recorded in the blockchain at every data center. When data is used for training, the hash will be regenerated and verified using blockchain technology. Figure 2.10, which follows, provides an illustration of datasets construction of NLP using blockchain. Data Data Data Data Research Center Data Hospital B Agreement for Participation in Dataset Building Hospital A Hospital C Diagnosis Laboratory Smart Contract Distributed Ledger Smart Contract Distributed Ledger Fig. 2.10 Datasets for NLP-based healthcare using blockchain 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 43. 30 For instance, if the research center requires access to the hospital’s datasets, it will first submit a smart contract request for data access. Hospitals can reply by stat- ing their permission to take part in dataset creation and providing any guidelines or limitations for data sharing and AI model training. A copy of the distributed ledger is accessible to each participant. In order to do future integrity checks on the data at each station, a hash code can be created, which can be saved in the blockchain. With the help of blockchain, a large number of data stations can contribute to the creation of verified datasets. Learning Phase In order to develop an ML algorithm via the available data, distributed learning is used. Threats to federated learning include a broken node, trusting local gradients, and aggregating gradients globally. In federated learning, utilizing blockchain helps address these issues and defend the model from poisoning assaults. A smart contract can be used to start training so that it can verify the legitimacy of the participant. Then, through the block, local gradients at federated nodes will be transmitted. Local gradients will be secured on the blockchain to prevent modification and used for verification later. Using a consensus algorithm, blockchain network miners will validate and produce global gradients. This is how blockchain may give the feder- ated network validity. Each node saves the retrieved features in the distributed led- ger for later use and embeds them in vector space. The blockchain approach for the security of the classifier in NLP is shown in Fig. 2.11. The trained model’s output is influenced by how real the post-training input is. We could anticipate the NLP model malfunctioning for adversarial text input. We can attempt to reduce some adversarial assaults by utilizing the blockchain in NLP- based healthcare. When identifying hostile text, the version of characteristics col- lected from a dataset of blockchain is utilized for model training. A smart contract will produce word embedding for the supplied input. It will search the distributed ledger for a similar corpus of word embedding based on synonyms. The blockchain network’s miners then receive additional distributions of the resulting extracts. Miners will use a trained model to compute the outcome for assigned characteristics rather than using proof of work. After that, the result is distributed in the mine pool, and if the majority of them agree, then the outcome is consensually added to the chain. Hence, the model will be protected using this framework from adversarial assaults on text. Figure 2.12 represents the mentioned framework. 5.2 Machine Learning for Medical Imaging Machine learning applications such as computer vision have been used in healthcare for medical imaging. Computer vision is a visual application of AI and computer science that aims on enabling machines to decipher, understand, and analyze P. Shah et al.
- 44. 31 Blockchain Network Consensus Algorithm NLP data Text Node 1 Node 2 Node 3 Node 4 Protect NLP model Protect NLP Classifier Global market Distributed ledger Local Gradients Local NLP Models Smart Contract (Initiate Training) Fig. 2.11 Protection of training phase of NLP using blockchain perceptible data from the world [41]. It involves developing algorithms and tech- niques to enable computers to recognize and classify objects, understand scenes, track motion, and more using images and videos. The techniques involved in com- puter vision are object detection, image classification, object tracking, and semantic and instance segmentation. 5.2.1 Applications in Healthcare The interpretation and analysis of many types of real-world data are aided by intel- ligent intervention employing a brain-like structure and advanced technologies like machine learning and computer vision [42]. A scientific application of machine learning is computer vision, which uses collected sequences of movies and photos to identify things. Convolutional neural networks (CNNs), a machine learning algo- rithm created to analyze picture input, prioritizes different elements to identify one image from another. Similar to the connection pattern of neurons in the brain, CNNs have a structural design. Computers have long been able to analyze visual imagery in meaningful ways thanks to computer vision. Object classification, localization, and detection are the terms used to describe the processes of determining an object’s kind, location within an image, and both concurrently [43]. 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 45. 32 Fig. 2.12 Solution for NLP-based healthcare using blockchain technology According to the National Cancer Institute’s National Lung Screening Trial (NLST), low-dose CT, which is used for screening lung cancer, has caused a 20% reduction in mortality [44]. The use of smart monitoring has increased because of developments in computer vision. To anticipate generalized anxiety disorder (GAD), a new system using computer vision and ML is introduced [45]. Computer vision algorithms in an adult intensive care unit can recognize patient movement actions like getting the patient in and out of bed or a chair [46]. A substantial possibility exists for deep convolutional neural networks as a tool for ear-related diagnosis [47]. A comprehensive image processing system to forecast the viability of human embryos, researchers coupled computer vision methodologies with machine learn- ing and different techniques involving neural networks [48]. Using a computer vision technique, it is also possible to identify hip fractures from pelvic X-rays [49]. Recorded endoscopic pictures will be swiftly and precisely analyzed by the ground- breaking CNN approach to detect esophageal cancer [50]. Deep learning methods also enable the detection of intracranial hemorrhage (ICH) [51]. It is possible to make a diagnosis based on chest CT pictures, leading to a machine learning algo- rithm in a quick and automated diagnostic technique [52]. In order to decrease the chances of infection from the doctor to the patient COVID-19, a revolutionary visual SLAM algorithm may also follow and find robots in real-time environ- ments [53]. P. Shah et al.
- 46. 33 5.2.2 Adversarial and Defense Attacks The images in which pixels are purposefully disturbed to confuse and deceive mod- els while appearing correct to human sight are adversarial images. Adversarial images trick DNN because it is vulnerable to even the smallest input disturbance. Figure 2.13 displays the ways adversarial attacks can be done on an image. Several attack strategies, such as FGSM [54], BIM [55], and R + FGSM [56], cause the ML model to make wrong predictions and decreases the overall robust- ness of the model. Figure 2.14 provides an example of an adversarial attack on an X-ray image. 5.2.3 Blockchain Solutions for Computer Vision–Based Healthcare Data Layer Since adversarial attacks are more likely to target images, we frame solutions that emphasize preventive actions, as seen in Fig. 2.15. A blockchain-based system will be used to post images to IPFS. A file-sharing technique called IPFS can be used to store and transport large data. It uses cryptographic hashes, which can be stored in the blockchain easily. The generated hashes are utilized to ensure the authenticity of images. First, different hospitals and diagnostic centers that have the data will request to upload medical images on IPFS, which will be validated by a smart con- tract before uploading. Every image will have its unique hash, which will be stored in the blockchain. Users can approach IPFS with the hash code to access the image data set when needed. The detection of adversarial images will be easy as hash codes are extremely sensitive. In this way, the data set is secured at IPFS using blockchain. Learning Phase Computer vision uses dynamic data for visual inputs. The model is trained with image data sets. As shown in Fig. 2.16, security can be provided through block- chain. With proper authentication, the training in the research centers should be Fig. 2.13 Adversarial attack on image 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 47. 34 Adversarial Attack in healthcare application Computer Vision Hairline Fracture Perturbation Normal Fig. 2.14 Adversarial attack on computer vision–based healthcare application Fig. 2.15 Creating datasets for computer-based healthcare applications started using smart contracts. After the training, the features that are extracted are stored in the blockchain for later referral as a feature vector with the formula x = (x1, x2, x3,..., xn) T, where n is the number of features that are extracted, and T is the transposition operation. This architecture safeguards the whole computer vision training area. As a result, the features that the learning process retrieved will be preserved without tampering. P. Shah et al.
- 48. 35 IPFS Chaincode Medical Images Initiate Learning Research Center Distributed Ledger Protect the model of computer vision Protect the algorithm of computer vision Learned Featured Vector CV Learned Model CV Learning Process Fig. 2.16 Training phase of computer vision and its protection through blockchain The outcome generated should be clear and understandable, and it should pro- vide reasons or evidence to support the conclusion. The ability to use the model post-training is limited and controlled using smart contracts. With smart contracts, access to the trained model is controlled. Only licensed physicians and researchers have access to the model. To verify the precision of the model run, it will compare it to the feature vectors recorded in the distributed ledger. Providing limited access to the data will help to tamper-proof it and also make it available when needed. The metadata can be stored in the blockchain for further validation and verification. Fig. 2.17 shows the security of trained models through blockchain. 5.3 Audio-Based AI Technology Acoustic AI techniques are sound recognition AI technology that uses sound data to identify and classify sounds. These techniques have a huge potential in the health- care sector, such as in diagnostics, monitoring, and treatment. Acoustic AI tech- niques have become widely used in diagnosis and treatment in healthcare. Some of the techniques involved in acoustic AI are selective noise canceling, Hi-fi audio reconstruction, analog audio emulation, speech processing, and improved spatial simulation. 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 49. 36 Initiate Access to model Distributed Ledger Distributed Ledger Learned Featured Vector User with image input Knowledge Meta Data Comprehensive Diagnosis with explanation Fig. 2.17 Blockchain security for a trained model of computer vision 5.3.1 Applications in Healthcare Numerous software applications employ sophisticated AI algorithms and process digital signals to identify complete sleep schedules, track the rate of breathing, detect gasping and snorting, and recognize patterns of sleep apnea. These applica- tions then utilize this information to accurately measure a person’s respiratory rate while they are sleeping, all via smartphones. These applications combine active sonar and passive acoustic analysis. One of the application frameworks is “Firefly” [57]. A framework designed using neural networks (NNs) can distinguish between four different forms of auscultatory noises, including wheezes, rhonchi, fine crack- les, and coarse crackles, which reduces human mistakes during auscultation [58]. Researchers have developed classifiers using this technology that can distinguish between different respiratory illnesses in adults using the auditory features of coughs. Also, they have created synthetic cough samples for each significant respi- ratory ailment, using recent advancements. To help doctors, machine learning algo- rithms identify the earliest stages of pulmonary disease, for example, Cough GAN generates simulated coughs that mimic major pulmonary symptoms. By accurately and early diagnosing advanced respiratory illnesses such as chronic obstructive pul- monary disease, doctors will create the best preventative treatment programs and lower morbidity [59]. AI-based technologies are used for pediatric breath sound classification where the use of a CNN architecture (N-CNN) along with other CNN architectures can be applied to examine discomfort in babies through their sound of P. Shah et al.
- 50. 37 crying patterns. Results show that this method is a much more beneficial and viable, alternate to the method of evaluation used conventionally [60]. 5.3.2 Adversarial Attack Adversarial audio is any audio that contains disturbance, or noise, often known as adverse perturbations, and it can fool a variety of sound classification systems. Figure 2.18 shows the classification of adversarial attacks in audio signals. Some assaults aim to create an adversarial audio sample that closely resembles the origi- nal, but the learned model would classify it incorrectly. These assaults fall under the category of speech-to-label assaults. By using the actual audio and the required output label, the attacker can show how genetic algorithms can generate hostile audio samples without the use of gradients. It increases random noise while prevent- ing human awareness of it [61]. During the conversion of speech to text through acoustic processing, an adversary can attempt to manipulate the output to achieve a specific result. Such attacks are referred to as speech-to-text attacks. It is possible to alter the audio spectrum to obtain a desired output by introducing a minor distur- bance using optimization-based attacks [62]. Figure 2.19 provides an example of an adversarial attack on an acoustic technology application in the healthcare system. 5.3.3 Blockchain Solutions for Acoustic AI-Based Healthcare Data Layer Data resides locally on the computers of data owners, such as physicians, hospital staff, and laboratories database, similar to NLP-based healthcare. Figure 2.20 dem- onstrates the data layer construction using blockchain for acoustic AI-based health- care. As there are many IoMT technologies that can threaten the privacy of entities Fig. 2.18 Classification of adversarial attack on audio 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 51. 38 Adversarial Attack in healthcare application Acoustic AI Normal Acoustic Signal Perturbation Adversarial Acoustic Signal Fig. 2.19 Adversarial attack on acoustic-based healthcare application IOMT Devices Data Data Data Research Center Data Data Smart Contract Agreement for Participation in Dataset Building Smart Contract Distributed Ledger Smart Contract Distributed Ledger Diagnosis Laboratory Access Control List Hospital C Hospital B Hospital A Fig. 2.20 Blockchain and dataset construction in acoustic AI-based healthcare with data, blockchain can be used to prevent the risks. Smart contracts can be deployed for access control on acoustic data storage. Similarly, IoMT devices can be checked for proper registration and authentication procedure before contributing to the data layer. Sharing of data for data layer construction is similar to the NLP- based healthcare data layer construction framework described earlier. Learning Phase In order to train our acoustic AI model, a federal learning approach is adopted as the data is distributed. The nodes in the AI network are the data owners. For subsequent verification and reference, the features that were extracted from audio samples would be safely stored in distributed ledgers. Depending on the learning strategy, the audio sample’s extracted features can take on any shape. The job of creating the global model is driven by consensus algorithms, and each local gradient is kept in the blockchain. Hence, the distributed ledger containing the global model can be protected against several threats. Figure 2.21 shows how the model is protected with blockchain technology. P. Shah et al.
- 52. 39 Blockchain Network Global market Consensus Algorithm Distributed ledger Local Gradients Text Node 4 Node 3 Local acoustic AI Models Smart Contract (Initiate Training) Acoustic Data Protect Acoustic AI Classifier Protect Acoustic AI model Node 2 Node 1 Fig. 2.21 Blockchain security in the learning phase of healthcare using acoustic AI After training, smart contracts are used to limit users of the model via authentica- tion protocols. A consensus algorithm is used to check if the input is legit or has been tampered with as audio signals are static dependent, that is, the previous behavior impacts the current behavior. Figure 2.22 represents the blockchain frame- work designed to protect the model of acoustic AI after the completion of the learn- ing phase. The user can get access to the model through a smart contract. It then proceeds to the consensus algorithm of the blockchain network where it is broken into N numbers and each fragment is given to the miners. The result is combined after the mining is completed. If the data has been adversary, then the result will not make any sense as it has a static dependency. Hence, it will help to detect any adver- sarial attack on the model. 6 Conclusion This chapter provides an outlook on AI-based healthcare technologies and their security through blockchain. Several research has been conducted in the field of AI and blockchain and their application. In this review, we have discussed different 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 53. 40 Fig. 2.22 Acoustic AI in healthcare and its security through blockchain fields of AI, which include machine learning through textual data (natural language processing), medical imaging, and acoustic AI in healthcare. We have also dis- cussed in our review various adversarial attacks and threats these sectors might face and its solution using blockchain technology. The potential that blockchain has in regard to security is undeniable. Blockchain is an immutable, distributed, decentral- ized ledger that contains the vast potential to safeguard the health sectors against different kinds of adversarial attacks, and privacy issues they might face during data storing and sharing. Figure 2.23 shows the overall properties and application of blockchain. In this chapter, we have discussed how blockchain can provide security in the data layer and training phases of the field mentioned. We have referred to various articles and review documents to collect information and conduct this research. Future research directions have been presented in this chapter for using blockchain in the field of AI and healthcare, which was developed through knowl- edge and information from current technologies, their application, threats, and existing challenges. P. Shah et al.
- 54. 41 Fig. 2.23 Blockchain for AI-based healthcare explained References 1. Yaeger, K., Martini, M., Rasouli, J., Costa, A. (2019). Emerging blockchain technology solutions for modern healthcare infrastructure. Journal of Scientific Innovation in Medicine, 2, 1–7. [CrossRef]. 2. Gupta, R., Reebadiya, D., Tanwar, S., Kumar, N., Guizani, M. (2021). When blockchain meets edge intelligence: Trusted and security solutions for consumers. IEEE Network, 35, 272–278. https://doi.org/10.1109/MNET.001.2000735. [CrossRef]. 3. Kumari, A., Gupta, R., Tanwar, S., Tyagi, S., Kumar, N. (2020). When Blockchain meets smart grid: Secure energy trading in demand response management. IEEE Network, 34, 299–305. https://doi.org/10.1109/MNET.001.1900660. [CrossRef]. 4. Wu, G., Wang, S., Ning, Z., Zhu, B. (2022). Privacy-preserved electronic medi- cal record exchanging and sharing: A blockchain-based smart healthcare system. IEEE Journal of Biomedical and Health Informatics, 26, 1917–1927. https://doi.org/10.1109/ JBHI.2021.3123643. [CrossRef] [PubMed]. 5. Rather, I. H., Idrees, S. M. (2021). Blockchain technology and its applications in the health- care sector. In Blockchain for healthcare systems (pp. 17–25). CRC Press. 6. Subramanian, G., Sreekantan Thampy, A. (2021). Implementation of Blockchain con- sortium to prioritize diabetes patients’ healthcare in pandemic situations. IEEE Access, 9, 162459–162475. https://doi.org/10.1109/ACCESS.2021.3132302. [CrossRef]. 2 Utilization of Blockchain Technology in Artificial Intelligence–Based Healthcare…
- 55. Random documents with unrelated content Scribd suggests to you:
- 56. THE FULL PROJECT GUTENBERG LICENSE
- 57. PLEASE READ THIS BEFORE YOU DISTRIBUTE OR USE THIS WORK To protect the Project Gutenberg™ mission of promoting the free distribution of electronic works, by using or distributing this work (or any other work associated in any way with the phrase “Project Gutenberg”), you agree to comply with all the terms of the Full Project Gutenberg™ License available with this file or online at www.gutenberg.org/license. Section 1. General Terms of Use and Redistributing Project Gutenberg™ electronic works 1.A. By reading or using any part of this Project Gutenberg™ electronic work, you indicate that you have read, understand, agree to and accept all the terms of this license and intellectual property (trademark/copyright) agreement. If you do not agree to abide by all the terms of this agreement, you must cease using and return or destroy all copies of Project Gutenberg™ electronic works in your possession. If you paid a fee for obtaining a copy of or access to a Project Gutenberg™ electronic work and you do not agree to be bound by the terms of this agreement, you may obtain a refund from the person or entity to whom you paid the fee as set forth in paragraph 1.E.8. 1.B. “Project Gutenberg” is a registered trademark. It may only be used on or associated in any way with an electronic work by people who agree to be bound by the terms of this agreement. There are a few things that you can do with most Project Gutenberg™ electronic works even without complying with the full terms of this agreement. See paragraph 1.C below. There are a lot of things you can do with Project Gutenberg™ electronic works if you follow the terms of this agreement and help preserve free future access to Project Gutenberg™ electronic works. See paragraph 1.E below.
- 58. 1.C. The Project Gutenberg Literary Archive Foundation (“the Foundation” or PGLAF), owns a compilation copyright in the collection of Project Gutenberg™ electronic works. Nearly all the individual works in the collection are in the public domain in the United States. If an individual work is unprotected by copyright law in the United States and you are located in the United States, we do not claim a right to prevent you from copying, distributing, performing, displaying or creating derivative works based on the work as long as all references to Project Gutenberg are removed. Of course, we hope that you will support the Project Gutenberg™ mission of promoting free access to electronic works by freely sharing Project Gutenberg™ works in compliance with the terms of this agreement for keeping the Project Gutenberg™ name associated with the work. You can easily comply with the terms of this agreement by keeping this work in the same format with its attached full Project Gutenberg™ License when you share it without charge with others. 1.D. The copyright laws of the place where you are located also govern what you can do with this work. Copyright laws in most countries are in a constant state of change. If you are outside the United States, check the laws of your country in addition to the terms of this agreement before downloading, copying, displaying, performing, distributing or creating derivative works based on this work or any other Project Gutenberg™ work. The Foundation makes no representations concerning the copyright status of any work in any country other than the United States. 1.E. Unless you have removed all references to Project Gutenberg: 1.E.1. The following sentence, with active links to, or other immediate access to, the full Project Gutenberg™ License must appear prominently whenever any copy of a Project Gutenberg™ work (any work on which the phrase “Project Gutenberg” appears, or with which the phrase “Project Gutenberg” is associated) is accessed, displayed, performed, viewed, copied or distributed:
- 59. This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. 1.E.2. If an individual Project Gutenberg™ electronic work is derived from texts not protected by U.S. copyright law (does not contain a notice indicating that it is posted with permission of the copyright holder), the work can be copied and distributed to anyone in the United States without paying any fees or charges. If you are redistributing or providing access to a work with the phrase “Project Gutenberg” associated with or appearing on the work, you must comply either with the requirements of paragraphs 1.E.1 through 1.E.7 or obtain permission for the use of the work and the Project Gutenberg™ trademark as set forth in paragraphs 1.E.8 or 1.E.9. 1.E.3. If an individual Project Gutenberg™ electronic work is posted with the permission of the copyright holder, your use and distribution must comply with both paragraphs 1.E.1 through 1.E.7 and any additional terms imposed by the copyright holder. Additional terms will be linked to the Project Gutenberg™ License for all works posted with the permission of the copyright holder found at the beginning of this work. 1.E.4. Do not unlink or detach or remove the full Project Gutenberg™ License terms from this work, or any files containing a part of this work or any other work associated with Project Gutenberg™. 1.E.5. Do not copy, display, perform, distribute or redistribute this electronic work, or any part of this electronic work, without prominently displaying the sentence set forth in paragraph 1.E.1
- 60. with active links or immediate access to the full terms of the Project Gutenberg™ License. 1.E.6. You may convert to and distribute this work in any binary, compressed, marked up, nonproprietary or proprietary form, including any word processing or hypertext form. However, if you provide access to or distribute copies of a Project Gutenberg™ work in a format other than “Plain Vanilla ASCII” or other format used in the official version posted on the official Project Gutenberg™ website (www.gutenberg.org), you must, at no additional cost, fee or expense to the user, provide a copy, a means of exporting a copy, or a means of obtaining a copy upon request, of the work in its original “Plain Vanilla ASCII” or other form. Any alternate format must include the full Project Gutenberg™ License as specified in paragraph 1.E.1. 1.E.7. Do not charge a fee for access to, viewing, displaying, performing, copying or distributing any Project Gutenberg™ works unless you comply with paragraph 1.E.8 or 1.E.9. 1.E.8. You may charge a reasonable fee for copies of or providing access to or distributing Project Gutenberg™ electronic works provided that: • You pay a royalty fee of 20% of the gross profits you derive from the use of Project Gutenberg™ works calculated using the method you already use to calculate your applicable taxes. The fee is owed to the owner of the Project Gutenberg™ trademark, but he has agreed to donate royalties under this paragraph to the Project Gutenberg Literary Archive Foundation. Royalty payments must be paid within 60 days following each date on which you prepare (or are legally required to prepare) your periodic tax returns. Royalty payments should be clearly marked as such and sent to the Project Gutenberg Literary Archive Foundation at the address specified in Section 4, “Information
- 61. about donations to the Project Gutenberg Literary Archive Foundation.” • You provide a full refund of any money paid by a user who notifies you in writing (or by e-mail) within 30 days of receipt that s/he does not agree to the terms of the full Project Gutenberg™ License. You must require such a user to return or destroy all copies of the works possessed in a physical medium and discontinue all use of and all access to other copies of Project Gutenberg™ works. • You provide, in accordance with paragraph 1.F.3, a full refund of any money paid for a work or a replacement copy, if a defect in the electronic work is discovered and reported to you within 90 days of receipt of the work. • You comply with all other terms of this agreement for free distribution of Project Gutenberg™ works. 1.E.9. If you wish to charge a fee or distribute a Project Gutenberg™ electronic work or group of works on different terms than are set forth in this agreement, you must obtain permission in writing from the Project Gutenberg Literary Archive Foundation, the manager of the Project Gutenberg™ trademark. Contact the Foundation as set forth in Section 3 below. 1.F. 1.F.1. Project Gutenberg volunteers and employees expend considerable effort to identify, do copyright research on, transcribe and proofread works not protected by U.S. copyright law in creating the Project Gutenberg™ collection. Despite these efforts, Project Gutenberg™ electronic works, and the medium on which they may be stored, may contain “Defects,” such as, but not limited to, incomplete, inaccurate or corrupt data, transcription errors, a copyright or other intellectual property infringement, a defective or
- 62. damaged disk or other medium, a computer virus, or computer codes that damage or cannot be read by your equipment. 1.F.2. LIMITED WARRANTY, DISCLAIMER OF DAMAGES - Except for the “Right of Replacement or Refund” described in paragraph 1.F.3, the Project Gutenberg Literary Archive Foundation, the owner of the Project Gutenberg™ trademark, and any other party distributing a Project Gutenberg™ electronic work under this agreement, disclaim all liability to you for damages, costs and expenses, including legal fees. YOU AGREE THAT YOU HAVE NO REMEDIES FOR NEGLIGENCE, STRICT LIABILITY, BREACH OF WARRANTY OR BREACH OF CONTRACT EXCEPT THOSE PROVIDED IN PARAGRAPH 1.F.3. YOU AGREE THAT THE FOUNDATION, THE TRADEMARK OWNER, AND ANY DISTRIBUTOR UNDER THIS AGREEMENT WILL NOT BE LIABLE TO YOU FOR ACTUAL, DIRECT, INDIRECT, CONSEQUENTIAL, PUNITIVE OR INCIDENTAL DAMAGES EVEN IF YOU GIVE NOTICE OF THE POSSIBILITY OF SUCH DAMAGE. 1.F.3. LIMITED RIGHT OF REPLACEMENT OR REFUND - If you discover a defect in this electronic work within 90 days of receiving it, you can receive a refund of the money (if any) you paid for it by sending a written explanation to the person you received the work from. If you received the work on a physical medium, you must return the medium with your written explanation. The person or entity that provided you with the defective work may elect to provide a replacement copy in lieu of a refund. If you received the work electronically, the person or entity providing it to you may choose to give you a second opportunity to receive the work electronically in lieu of a refund. If the second copy is also defective, you may demand a refund in writing without further opportunities to fix the problem. 1.F.4. Except for the limited right of replacement or refund set forth in paragraph 1.F.3, this work is provided to you ‘AS-IS’, WITH NO OTHER WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED,
- 63. INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY PURPOSE. 1.F.5. Some states do not allow disclaimers of certain implied warranties or the exclusion or limitation of certain types of damages. If any disclaimer or limitation set forth in this agreement violates the law of the state applicable to this agreement, the agreement shall be interpreted to make the maximum disclaimer or limitation permitted by the applicable state law. The invalidity or unenforceability of any provision of this agreement shall not void the remaining provisions. 1.F.6. INDEMNITY - You agree to indemnify and hold the Foundation, the trademark owner, any agent or employee of the Foundation, anyone providing copies of Project Gutenberg™ electronic works in accordance with this agreement, and any volunteers associated with the production, promotion and distribution of Project Gutenberg™ electronic works, harmless from all liability, costs and expenses, including legal fees, that arise directly or indirectly from any of the following which you do or cause to occur: (a) distribution of this or any Project Gutenberg™ work, (b) alteration, modification, or additions or deletions to any Project Gutenberg™ work, and (c) any Defect you cause. Section 2. Information about the Mission of Project Gutenberg™ Project Gutenberg™ is synonymous with the free distribution of electronic works in formats readable by the widest variety of computers including obsolete, old, middle-aged and new computers. It exists because of the efforts of hundreds of volunteers and donations from people in all walks of life. Volunteers and financial support to provide volunteers with the assistance they need are critical to reaching Project Gutenberg™’s goals and ensuring that the Project Gutenberg™ collection will
- 64. remain freely available for generations to come. In 2001, the Project Gutenberg Literary Archive Foundation was created to provide a secure and permanent future for Project Gutenberg™ and future generations. To learn more about the Project Gutenberg Literary Archive Foundation and how your efforts and donations can help, see Sections 3 and 4 and the Foundation information page at www.gutenberg.org. Section 3. Information about the Project Gutenberg Literary Archive Foundation The Project Gutenberg Literary Archive Foundation is a non-profit 501(c)(3) educational corporation organized under the laws of the state of Mississippi and granted tax exempt status by the Internal Revenue Service. The Foundation’s EIN or federal tax identification number is 64-6221541. Contributions to the Project Gutenberg Literary Archive Foundation are tax deductible to the full extent permitted by U.S. federal laws and your state’s laws. The Foundation’s business office is located at 809 North 1500 West, Salt Lake City, UT 84116, (801) 596-1887. Email contact links and up to date contact information can be found at the Foundation’s website and official page at www.gutenberg.org/contact Section 4. Information about Donations to the Project Gutenberg Literary Archive Foundation Project Gutenberg™ depends upon and cannot survive without widespread public support and donations to carry out its mission of increasing the number of public domain and licensed works that can be freely distributed in machine-readable form accessible by the widest array of equipment including outdated equipment. Many
- 65. small donations ($1 to $5,000) are particularly important to maintaining tax exempt status with the IRS. The Foundation is committed to complying with the laws regulating charities and charitable donations in all 50 states of the United States. Compliance requirements are not uniform and it takes a considerable effort, much paperwork and many fees to meet and keep up with these requirements. We do not solicit donations in locations where we have not received written confirmation of compliance. To SEND DONATIONS or determine the status of compliance for any particular state visit www.gutenberg.org/donate. While we cannot and do not solicit contributions from states where we have not met the solicitation requirements, we know of no prohibition against accepting unsolicited donations from donors in such states who approach us with offers to donate. International donations are gratefully accepted, but we cannot make any statements concerning tax treatment of donations received from outside the United States. U.S. laws alone swamp our small staff. Please check the Project Gutenberg web pages for current donation methods and addresses. Donations are accepted in a number of other ways including checks, online payments and credit card donations. To donate, please visit: www.gutenberg.org/donate. Section 5. General Information About Project Gutenberg™ electronic works Professor Michael S. Hart was the originator of the Project Gutenberg™ concept of a library of electronic works that could be freely shared with anyone. For forty years, he produced and distributed Project Gutenberg™ eBooks with only a loose network of volunteer support.
- 66. Project Gutenberg™ eBooks are often created from several printed editions, all of which are confirmed as not protected by copyright in the U.S. unless a copyright notice is included. Thus, we do not necessarily keep eBooks in compliance with any particular paper edition. Most people start at our website which has the main PG search facility: www.gutenberg.org. This website includes information about Project Gutenberg™, including how to make donations to the Project Gutenberg Literary Archive Foundation, how to help produce our new eBooks, and how to subscribe to our email newsletter to hear about new eBooks.
- 67. Welcome to our website – the perfect destination for book lovers and knowledge seekers. We believe that every book holds a new world, offering opportunities for learning, discovery, and personal growth. That’s why we are dedicated to bringing you a diverse collection of books, ranging from classic literature and specialized publications to self-development guides and children's books. More than just a book-buying platform, we strive to be a bridge connecting you with timeless cultural and intellectual values. With an elegant, user-friendly interface and a smart search system, you can quickly find the books that best suit your interests. Additionally, our special promotions and home delivery services help you save time and fully enjoy the joy of reading. Join us on a journey of knowledge exploration, passion nurturing, and personal growth every day! ebookbell.com