COMBINING BLOCKCHAIN AND IOT FOR DECENTRALIZED HEALTHCARE DATA MANAGEMENT

International Journal on Cryptography and Information Security (IJCIS), Vol. 13, No.1, March 2023
DOI:10.5121/ijcis.2023.13102 35
COMBINING BLOCKCHAIN AND IOT FOR
DECENTRALIZED HEALTHCARE
DATA MANAGEMENT
Sajad Meisami1
, Sadaf Meisami2
, Melina Yousefi3
and Mohammad Reza Aref4
1
Department of Computer Science, Illinois Institute of Technology, Chicago, USA
2
Department of Management, Kharazmi University, Tehran, Iran
3
Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
4
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
ABSTRACT
The emergence of the Internet of Things (IoT) has resulted in a significant increase in research on e-health.
As the amount of patient data grows, it has become increasingly challenging to protect patients' privacy.
Patient data is commonly stored in the cloud, making it difficult for users to control and protect their
information. Moreover, the recent rise in security and surveillance breaches in the healthcare industry has
highlighted the need for a better approach to data storage and protection. Traditional models that rely on
third-party control over patients' healthcare data are no longer reliable, as they have proven vulnerable to
security breaches. To address these issues, blockchain technology has emerged as a promising solution.
Blockchain-based protocols have the potential to provide a secure and efficient system for e-health
applications that does not require trust in third-party intermediaries. The proposed protocol outlined in
this paper uses a blockchain-based approach to manage patient data securely and efficiently. Unlike
Bitcoin, which is primarily used for financial transactions, the protocol described here is designed
specifically for e-health applications. It employs a consensus mechanism that is more suitable for resource
constrained IoT devices, thereby reducing network costs and increasing efficiency. The proposed protocol
also provides a privacy-preserving access control mechanism that enables patients to have more control
over their healthcare data. By leveraging blockchain technology, the protocol ensures that only authorized
individuals can access the patient's data, which helps prevent data breaches and other security issues.
Finally, the security and privacy of the proposed protocol are analysed to ensure that it meets the
necessary standards for data protection. The protocol's effectiveness and efficiency are tested under
different scenarios to ensure that it can perform reliably and consistently. Finally, the protocol proposed in
this paper shows that how blockchain can be used to provide a secure and efficient system that empowers
patients to take control of their healthcare data.
KEYWORDS
Internet of Things (IoT), Blockchain Technology, Healthcare data management, IoT e-health, privacy,
access control, Security
1. INTRODUCTION
The world is experiencing a significant increase in the number of patients, and the availability of
primary doctors or medical staff is becoming more challenging. This situation has led to a
growing interest in using the Internet of Things (IoT) in healthcare to improve medical services.
The IoT allows any device to connect to other devices and the internet at any time and place,
making it a powerful tool in addressing the challenges in the healthcare programmes. According
to research, over 75 billion devices will be able to connect to the internet by 2025, presenting vast
opportunities for IoT applications in e-health [1]. E-health is one of the main applications of IoT,

36
and it offers many benefits for both medical staff and patients. One significant advantage of e-
health is that it allows medical staff to treat more patients while providing more convenience for
patients. With e-health, patients can stay connected with their doctors or medical staff as
required, reducing the need for frequent visits to healthcare facilities. This, in turn, reduces
medical costs and improves the quality of care and treatment. Additionally, e-health can help
medical staff to monitor patient health more effectively, leading to early detection of health issues
and timely intervention.
The advent of the Internet of Things (IoT) has revolutionized the healthcare programmes, and
wearable devices have emerged as a significant contributor to the development of e-health.
Wearable devices like smartwatches and fitness trackers can measure a patient's vital signs,
including heart rate, blood glucose, body temperature, and blood pressure, among others. This
capability has opened a vast array of opportunities for remote patient monitoring and disease
management. One of the most significant benefits of wearable devices is that they collect patient
health data automatically, eliminating the need for manual data collection by healthcare
providers. This data is then transmitted to central storage or the cloud, where it can be analysed
and processed by medical staff to facilitate health monitoring, disease diagnosis, and treatment.
The use of wearable devices for health monitoring has made it easier for medical staff to provide
personalized care to patients, allowing them to monitor the patient's health status in real-time and
make timely interventions when necessary. Furthermore, the collected data can be used to detect
patterns and trends in a patient's health status over time. This information can be analyzed to
predict future health problems, enabling medical staff to provide proactive care and treatment.
Wearable devices also allow patients to monitor their own health, encouraging them to take an
active role in managing their health and well-being [2].
The protection of patients' healthcare data is of utmost importance as it contains sensitive and
confidential information that must be kept secure. However, with the increasing use of
technology in the healthcare application, storing and processing patients' data remotely has
become a common practice. This has led to concerns about the privacy and confidentiality of
patients' healthcare data, as the data is vulnerable to unauthorized access and malicious attacks.
One of the most significant risks associated with remote data storage and processing is the
potential for security breaches. Malicious actors can exploit vulnerabilities in the system to
access patients' data, and once they gain access, they can modify or replace the data with
incorrect information or steal it entirely. This type of security attack can have severe
consequences for patients, leading to misdiagnosis, mistreatment, and potential harm [3].
A. Organization
Section II describes the related work involved. Section III discusses the challenges we solve in
this paper. Sections IV provides an overview of the system model, whereas section V explains the
network protocol in detail. Section VI discusses the security and privacy of the proposed model,
and section VII provides a conclusion.
2. RELATED WORKS
To address the privacy issue on personal data, researchers developed various methods. One of
them is data anonymization that attempts to protect personally identiﬁable information. For
example, in the k-anonymity method used in anonymous datasets, any necessary recorded data is
indistinguishable from at least k−1 other important recorded data [4]. However, in recent
research, it has been shown, anonymized datasets can be broken even with a little information
(their anonymity disappears) [5]. There exist other privacy-preserving methods like differential
privacy, that perturbs data, or adds noise to the computational process before sharing the data [6]

37
and practices on creating noisy data or summarizing [7]. These methods are not efficient in
healthcare applications where patients’ original data are required to send to medical staff for
medical treatments.
Attribute-based encryption (ABE) is a useful technology that can provide data privacy and fine-
grained access control when users want to secretly share data stored in a third-party cloud server
[8]. Almost all ABE schemes require a trusted private key generator (PKG) to set up the system
and distribute for users the corresponding secret key [9]. However, in all ABE schemes, the PKG
can decrypt all data stored in the cloud server, which may cause serious problems such as privacy
data leakage and key abuse. Furthermore, the traditional cloud storage model runs in a centralized
storage manner, so existence a single point of failure can collapse the system. Other encryption
schemes exist that allow running computations and queries over encrypted data that called fully
homomorphic encryption (FHE) [10] but are currently too unsuitable to use in practice widely.
In recent years, decentralized cryptocurrency systems have emerged. Bitcoin was the first of these
systems, which use blockchain technology. Bitcoin allows users securely to make transactions
and transfer currency (bitcoins) with others without the need to trusted third-party [11].
Blockchain works as an immutable timestamp ledger of blocks that are shared across all
participating nodes in the network, which can bypass the need for a central authority [12]. This
technology is used for sharing and storing data in a distributed manner by a peer-to-peer network
[13]. Nowadays, blockchain is playing an effective role in financial transactions [14]. Also, it can
be a facilitator in many other fields. Such as decentralized IoT [15], identity-based PKI [16],
decentralized supply chain [17], decentralized proof of document existence [18], decentralized
storage [19–21], etc.
In [22], the work by Zyskind et al. has shown the use of blockchain technology to construct an
access control and management platform for personal data. They focused on users' privacy. And
they combine blockchain and off-chain storage to storing encrypted data out of blockchain ledger
while pointers to the encrypted data exist on the blockchain. Recently researchers addressed the
security and privacy issues on healthcare data, using blockchain technology, and proposed new
schemes [23–26].
In this paper, we combine IoT and blockchain technology to construct a novel platform for
patients' healthcare data management that satisfies privacy and security requirements.
3. CHALLENGES AND OUR SOLUTIONS
The primary focus of e-health applications in the IoT is to ensure the secure transmission and
preservation of patients' healthcare data. The decentralized nature of blockchain technology,
along with its attributes such as immutability and transparency, make it a promising solution for
achieving these goals. However, despite its potential benefits, there are still challenges associated
with the implementation of blockchain technology in the IoT. These challenges may include
resource constraints, scalability issues, and high computational requirements. In this context, it is
crucial to develop effective solutions to address these challenges and enable the successful
implementation of blockchain technology in e-health IoT applications.
3.1. Scalability
Due to their limited resources, IoT devices face challenges in executing computationally
intensive tasks, such as solving problems for adding new blocks to the blockchain ledger through
consensus algorithms. Proof-of-Work (PoW) is a commonly used consensus algorithm, but its
high computational requirements make it unsuitable for resource-constrained IoT devices. To

38
address this challenge, we have adopted an alternative consensus method called Practical
Byzantine Fault Tolerance (PBFT) for our blockchain network [27]. PBFT is a voting-based
consensus method that involves multiple rounds of voting by all nodes in the network [28]. By
eliminating the need for PoW, we can significantly reduce the network costs associated with
consensus operations, including bandwidth, processor requirements, and energy consumption.
Overall, using PBFT as a consensus method allows us to increase the network's efficiency while
minimizing the resource demands on IoT devices. This approach can help ensure the smooth
operation of e-health applications on IoT devices, which rely on the efficient processing and
transmission of sensitive patient data.
3.2. Data Storage
Storing IoT big data directly on the blockchain ledger is not a practical or suitable approach due
to a large amount of storage space required. To address this challenge, we have developed a
solution that involves storing only pointers to the data (hashed encrypted data) on the blockchain
while storing the actual data (encrypted form) on off-chain storage, as detailed in section IV, E.
Off-chain Storage. By using this approach, we can significantly reduce the storage requirements
of the blockchain while still ensuring that the data is securely stored and accessible. The use of
off-chain storage for IoT big data also helps to minimize the processing and bandwidth demands
on IoT devices, enabling them to operate more efficiently. Overall, this approach to storing IoT
big data strikes a balance between the need for secure and accessible data and the practical
limitations of blockchain technology. It represents an innovative solution to a significant
challenge in the development of e-health applications on IoT devices.
3.3. Security of Data
To ensure the security of patients' healthcare data, we employ a symmetric key encryption
scheme, which is a cryptographic technique that provides confidentiality and integrity to data. In
this scheme, the data is encrypted using a symmetric key, and only authorized parties can access
this key for decryption. We use this encryption technique to protect the data during transmission
and storage. As a result, even when the data is stored off-chain, it remains encrypted, and only
authorized parties with the key can access and decrypt the data. This approach provides an
additional layer of protection to sensitive healthcare data, ensuring that only authorized parties
can access it.
3.4. Patients' Privacy
The main concern that is addressed in this paper is preserving patients' privacy because Patients'
healthcare data is highly privacy-sensitive. We assume that the medical staff is honest-but-
curious (i.e., they follow the protocol). In our system, patients could remain (pseudo) anonymous.
At the same time, medical staff proﬁles could be stored on the blockchain so that patients can
trust medical staff by verifying medical staff identities. Our proposed platform satisfies the
following Items:
1) Patients' Data Ownership: Our proposed platform upholds the concept of patients' data
ownership, where patients have the sole authority to control their healthcare data. In this
system, we acknowledge medical staff as Service Providers who are granted specific
permissions set by the patients. Therefore, patients hold the position of healthcare data
owners and have complete control over their data. This approach enables patients to
maintain their privacy and have the freedom to share their healthcare data with selected
medical staff, thereby ensuring a high level of trust between patients and medical staff.

39
2) Fine-grained Access Control: Each patient can grant a set of permissions to Any desired
member of the medical staff for accessing a patient's healthcare data. Also, the patient can
alter or revoke the set of granted access permissions. These permissions are securely stored
on blockchain ledger as access-control policies, where only the patient can change or revoke
them.
3) Data Transparency and Auditability: Patients have complete and accurate transparency over
their collected healthcare data, and they can know how medical staff can access to which
part or type of their healthcare data.
4. OUR SYSTEM MODEL
As demonstrated in Figure 1, our proposed system model consists of the following main modules:
1) Wearable IoT devices.
2) Patient’s smartphone.
3) Medical staff.
4) Blockchain.
5) Off-chain storage.
Medical
staff
blockchain
Patient Wearable IoT devices
and smartphone
Off-chain Storage
Data+Policies
Data
Fig. 1. Our proposed system model.
4.1. Wearable IoT devices
The healthcare data collected by wearable IoT devices include vital signs such as blood pressure,
body temperature, and heart rate. However, these devices have resource limitations, such as low
energy, processing power, and storage space. To address these limitations, the collected data is
sent to the patient's smartphone through short-range communication methods such as Bluetooth
or Zigbee. This allows wearable devices to conserve their resources while still collecting the
necessary data, which can then be transmitted to the blockchain network for further analysis.

40
4.2. Patient’s Smartphone
The computing capabilities of smartphones are greater than those of IoT devices, as smartphones
have more storage space, longer battery life, and higher processing power. Therefore,
smartphones can perform more complex tasks, such as computational and cryptographic
operations. Additionally, smartphones can transmit data using long-range communication
methods, such as cellular networks, and act as a gateway, enabling patients to connect with the
blockchain network.
4.3. Medical Staff
Medical staff, including physicians and nurses, should receive patients' healthcare data and, after
analyzing it, obtain information about the patient's health status. Then they provide appropriate
treatment for patients.
4.4. Blockchain
In simple terms, blockchain is a decentralized and secure digital ledger that maintains a record of
transactions. It is a chain of blocks, where each block contains a set of transactions and a
cryptographic hash of the previous block. Blockchain technology is designed to be tamper-proof,
making it highly secure and resistant to any unauthorized modifications. Because of its
transparency and decentralized nature, blockchain is often used to facilitate secure transactions
between parties without the need for intermediaries.
In our work, we use blockchain to store access policies and eliminate the need for a third-party
that preserves network against DoS attack and single point of failure. It also ensures the
availability and integrity of the patients' data. The data is not stored on the blockchain, but only
the pointers to the data (hash of encrypted data) are stored on it to lighten the storage space of
blockchain. Also, because the use of PoW in IoT applications is not appropriate, we use PBFT for
consensus operations.
4.5. Off-Chain Storage
We store patients' encrypted data on off-chain storage. For the implementation of off-chain
storage, we use The InterPlanetary File System (IPFS) [21] that is a peer-to-peer distributed ﬁle
system that seeks to connect all computing devices with the same system of ﬁles. It provides a
high throughput content-addressed block storage model, with content-addressed hyperlinks. IPFS
combines a distributed hashtable (or DHT), an incentivized block exchange, and a self-certifying
namespace. IPFS has no single point of failure, and nodes do not need to trust each other [21]. In
IPFS, we distribute the data and store them on different servers all over the world. Not using the
central server is the reason for the advantage of IPFS over conventional cloud storage.
5. THE NETWORK PROTOCOL
In this section, we describe the our system protocol design to ensure the privacy and security of
patients' healthcare data while allowing medical staff to access and analyze it for the purpose of
providing appropriate treatment.
5.1. Cryptographic Techniques used in the System
Hash Function: We use SHA-256 to implement the hash function (H indicates the hash function).
SHA-256 (Secure Hash Algorithm 256-bit) is a cryptographic hash function that takes input data

41
of variable length and produces a fixed-length output of 256 bits. It is one of the most widely
used cryptographic hash functions and is commonly used to ensure data integrity and message
authentication [29]. In order to perform its cryptographic functions, SHA-256 breaks down input
data into blocks of a fixed size and processes each block through a series of mathematical
operations. The output of each block's processing is combined with the result of the previous
block until the entire message has been processed. Regardless of the input data's size, the output
of SHA-256 is a fixed-length string of 64 hexadecimal characters that is unique to the input data.
To ensure data integrity and prevent tampering, SHA-256 provides a secure hashing function that
cannot be reversed to obtain the original input data from the output hash. This feature makes it a
suitable choice for various security applications, including digital signatures algorithms and data
integrity checks.
Symmetric Key Encryption: Symmetric algorithm uses the same key for encryption of plaintext
and decryption of ciphertext. We use AES to implement the symmetric key encryption. AES
(Advanced Encryption Standard) [30] is a popular symmetric block cipher encryption algorithm
that was introduced by the National Institute of Standards and Technology (NIST) in 2001 to
replace the less secure Data Encryption Standard (DES). AES is widely used to secure sensitive
data in various applications, including electronic communication, and banking applications. To
encrypt and decrypt data, AES employs a symmetric key, which means that the same key is used
for both encryption and decryption. The key length, which can be either 128, 192, or 256 bits,
determines the strength of the encryption. AES operates by dividing the plaintext into fixed-sized
blocks and then performing a series of mathematical operations on each block known as rounds.
The number of rounds required is determined by the key length, with 10 rounds for 128-bit keys,
12 rounds for 192-bit keys, and 14 rounds for 256-bit keys. The security of AES is based on the
fact that it is computationally infeasible to determine the private key which is used for encryption
from the ciphertext without knowledge of the key. This fact makes AES a strong encryption
algorithm that perfectly functions in real-world applications and is widely used to secure sensitive
information. (Genc indicates generating algorithm).
Digital Signature Scheme: A digital signature is a cryptographic technique used to verify the
authenticity and integrity of a digital document or message. It involves generating a unique
digital signature using a mathematical algorithm, which can only be created by the owner of a
private key in a public key infrastructure (PKI). The process of creating a digital signature
involves first generating a hash of the message using a hashing algorithm like SHA-256.
Afterward, the sender encrypts the hash value using their private key to produce a unique digital
signature. This digital signature is then appended to the message together with the sender's public
key, which can be used to authenticate the signature's origin and verify the integrity of the
message. After receiving the message with the digital signature and the sender's public key, the
recipient can use the public key to decrypt the signature and obtain the hash value of the message.
The recipient then generates a hash of the received message using the same hashing algorithm
and compares it with the hash value obtained from the digital signature. If the two hash values
match, the recipient can be assured that the message has not been altered during transmission and
that the signature was created by the sender with their private key, which confirms the sender's
authenticity. ECDSA stands for Elliptic Curve Digital Signature Algorithm. It is a type of digital
signature algorithm that uses elliptic curve cryptography to provide authentication and integrity
for digital documents or messages. ECDSA is widely used in applications such as digital identity
verification, financial transactions, and secure communication, due to its strong security
properties and efficient use of resources. In our protocol Digital signature is added to the data for
authentication purposes. For the implementation of the digital signature, we use ECDSA with a
secp256k1 curve [31] (Gsig indicates generating algorithm).

42
5.2. Protocol in Detail
staff m each generates a pair of private and public keys to sign and send transactions to the
blockchain network and announce their public key (as their address) on the network. Patient P
also generates a secret key for encrypting data with an AES symmetric encryption algorithm.
Then the patient shares the secret key with the chosen member of the medical staff so that, later,
she will be able to decrypt her authorized data with that secret key.
Protocol 1 Joining the Blockchain
1: procedure GENERATING(p,m)
2: p executes:
3: (pkp
sig, skp
sig) ←Gsig( )
4: skp,m
enc ←Genc( )
5: p shares pkp
sig (as address) on the network
6: m executes:
7: (pkm
sig, skm
sig) ←Gsig( )
8: m shares pkm
sig (as address) on the network
9: p shares skp,m
enc with m from secure channel
10: // Both p and m have skp,m
enc
11: return pkp
sig, pkm
sig, skp,m
enc
12: end procedure
Registration of access policy: We denote the data access permissions by POLICYp,m, which
indicates the permissions that the patient p gives to the selected member of the medical staff m so
that she can access a particular type or all of the patient's data. For example, POLICYp,m = {body
temperature, blood pressure}.
Protocol 2 Access Control Transaction
1: procedure ACCESSTX(pkk
sig ,message)
2: s ← 0
3: (pkp
sig || pkm
sig || POLICYp,m ) = message
4: if pkk
sig = pkp
sig then
5: L[H(pkk
sig)] ← L[H(pkk
sig)] ∪ message
6: // L is Blockchain memory
7: s ← 1
8: end if
9: return s
10: end procedure
By sending a Taccess transaction on the blockchain network that contains POLICYp,m, the patient
gives the desired permissions to the medical staff. As illustrated in protocol 2, this transaction is
performed in the nodes of the blockchain, and it is checked the patient himself has sent the
transaction, then the set of permissions are stored on the blockchain ledger.
The patient can send new Taccess transactions and change the permissions set granted to the
medical staff. Also, Sending the empty set by the patient can revoke all access-permissions set
previously granted.
Data storage and retrieval: Tdata transaction is used to store patients' encrypted healthcare data on
off-chain storage (IPFS) or access stored data and receive it. The patient (to store and retrieve the

43
data) and the medical staff (only to retrieve the data) can send the Tdata transaction to the
blockchain network.
If the Tdata transaction (by the patient or the medical staff) is sent to the network, the nodes in the
blockchain first check with the following protocol (protocol3) whether they have access
permissions or not?
Protocol 3 Blockchain Permissions Checking
1: procedure POLICYCHECK(pkk
sig ,T) //T=type of data
2: s ← 0
3: if L[H(pkk
sig)] ≠ ∅ then
4: (pkp
sig || pkm
sig || POLICYp,m )← L[H(pkk
sig)]
5: if pkk
sig=pkp
sig or
6: (pkk
sig=pkm
sig and T ∈ POLICYp,m) then
7: s ← 1
8: end if
9: end if
10: return s
11: end procedure
Now, after checking the access permissions and the transaction sender's approval, he or she can
store or retrieve patients' encrypted healthcare data with the following protocol.
Protocol 4 Data Transaction
1: procedure DATATX(pkk
sig ,message)
2: (C || T || RW) = message
3: // C=encrypted data , T=type of data
4: //RW=read data(=1) or write data(=0)
5: if POLICYCHECK(pkk
sig , T)=True then
6: (pkp
sig || pkm
sig || POLICYp,m ) ← L[H(pkk
sig)]
7: if RW = 0 then
8: L[pkk
sig || T] ← L[pkk
sig || T] ∪ H(C)
9: (IPFS) ds[H(C)] ← C
10: return H(C)
11: else if C ∈ L[pkk
sig || T] then
12: (IPFS) return ds[H(C)]
13: end if
14: end if
15: return ∅
16: end procedure
Note that we used IPFS shorthand notation in lines 9 and 12 of Protocol 4 for accessing the off-
chain storage. IPFS instruction cause to send Off-blockchain network message in off-chain
storage for storing or retrieving data.
With the above protocols, the patient can easily upload the encrypted healthcare data in the
network. The chosen members of the medical staff can also receive the encrypted data if there is
a permission, and then with the skp,m
enc that they have received before (in protocol 1), they can
decrypt encrypted data and access the original healthcare data.

44
6. SECURITY AND PRIVACY ANALYSIS
In this section, we discuss and investigate the performance of our proposed protocol in terms of
security and privacy. For security designing in any model, there are exist three main security
requirements that need to be addressed: Confidentiality, Integrity, and Availability, known as
CIA [32]. Confidentiality makes sure that the system's messages should be read by only
authorized users who can access the system. The data integrity is responsible for making sure that
no one without permission can change the stored data, and the availability of the data means that
when users needed to the data, it is available to them. Now, we summarize the aforementioned
primary security requirement evaluation in Table 1.
Table 1. Security Requirement Analysis.
Requirement Model Solution
Confidentiality Achieved by using symmetric key encryption.
Integrity Hashing of data blocks in blockchain is employed to achieve integrity.
Availability Achieved by limiting acceptable transactions in the network.
Authorization Using digital signature to achieve authorization.
In our model, data ownership is emphasized for preserving privacy. That means only the patients
(users) can control their data. The decentralized nature of the blockchain technology and using
digital signature to sign transactions in the network ensure that an adversary cannot be able to
infiltrate the system as a user. Because gaining control over the majority of the network’s
resources (at least 51%) or forging digital-signature is almost impossible for the adversary. Also,
the model ensures other privacy-preserving parameters that we previously mentioned (in section
III) like, Data Transparency and Auditability. Fine-grained access control is satisfied by storing
access-control policies on a blockchain ledger, where only the patient can change or revoke them.
In Table 2, we have made a comparison between our proposed model and other existing systems
and show how are work outperformed the other existing works. One of the important points is
that our proposed model is using decentralized and distributed off-chain storage space to prevent
single point of failure.
Table 2. Comparison of Different Existing Systems.
Model Name
Property Yang[33]
Xia-
I[34]
Xia-
II[36]
Peterson[38] Zang[40] A.Zhang[42]
Our
Proposed
Model
Access
control
      
Blockchain-
Based
×    ×  
Privacy
Preserving
      
IPFS Off-
chain storage
× × × × × × 

45
Now, we describe a few significant real-world attacks and analyze the resilience of our model
against each of them.
6.1. Denial of Service (DOS) Attack
Generally, a DoS (Denial of Service) attack is a malicious cyber-attack that disrupts the regular
operation of a system or network by inundating it with an excessive amount of traffic or requests.
The objective is to render the targeted system or network inaccessible to legitimate users by using
up its resources, such as bandwidth or processing power. This can result in a significant impact
on the target's business, finances, or reputation. Attackers can use different tactics, such as
flooding the target with traffic, exploiting vulnerabilities in the system, or leveraging malware to
control a network of compromised devices to carry out the attack. Specially, in our case of study
an attacker generates a large number of transactions to increase trafﬁc in the network and disrupt
the blockchain.
Defence: Only two types of transactions can be sent in the network. Also, each node can send a
limited number of transactions, and the blockchain network will reject the rest of the user's
transactions after receiving a few messages from a specific node. So, our proposed model has
high resilience against DoS attack.
6.2. Modification Attack
A modification attack is a form of cyber-attack where the attacker tries to modify data or
information in transit between two parties, often without being detected. The attack can happen at
any stage in the communication process, from transmission over a network to storage on a device.
The objective of a modification attack is to modify the data in a manner that benefits the attacker,
such as changing the message's contents, redirecting the destination of a transfer, or manipulating
the values of financial transactions. To prevent modification attacks, techniques such as
encryption and digital signatures can be employed to ensure the data's integrity and authenticity
during transmission. Specially, in our case of study an attacker tries to modify or remove the
stored patients' data (like access policies and hash) on the blockchain ledger.
Defence: Blockchain, by using an immutable ledger and other techniques such as encryption and
digital signatures can ensure the data's integrity and authenticity during transmission and create
high resistance against modification attack.
6.3. Public Blockchain Modification
A public blockchain can also be vulnerable to modification attacks, where an attacker tries to
alter the contents of a block in the chain, resulting in the creation of a new invalid chain or a fork
in the existing chain. Public blockchains are decentralized and transparent ledgers that allow
anyone to participate in the network.
Defence: To prevent modification attacks, a consensus mechanism is used to ensure that all nodes
in the network agree on the state of the chain, and any modifications require validation by the
majority of nodes in the network. We used PBFT consensus method in our protocol. Also, we can
use the private type of blockchain, so the nodes are from outside the organization cannot work as
miners to create a malicious block.
6.4. Storage Attack

46
Generally, A storage attack is a cyber-attack that involves unauthorized access to data that is
stored on a system or device. These attacks can be carried out using various methods, such as
exploiting security vulnerabilities, using malware to access sensitive information, or physically
accessing the storage medium. The aim of a storage attack is to steal or manipulate the data that is
being stored, which could include financial information, personal data, or other valuable assets.
In our case of study, an attacker wants to remove, change, or add data in the Off-chain storage.
Defence: To prevent storage attacks, security measures such as encryption, access control, and
regular data backups can be implemented. In our system, on blockchain ledger exist a hash of the
encrypted data stored in the Off-chain storage; therefore, changes in the data can easily be
detected. So, there is a high resilience against this attack.
6.5. Appending Attack
An appending attack is a cybersecurity attack where an unauthorized user gains access to a
system or network and adds or appends data or commands to a file or program. The objective of
such an attack is to execute malicious code, steal sensitive data or take over the targeted system.
The attacker can append their own code to a program to manipulate the program's behaviour or
add commands to an existing script, thereby granting them unauthorized access to the system.
Specifically, in our study an attacker tries to compromise a miner and generate blocks with fake
transactions to create a false reputation.
Defence: Due to the usage of private blockchain, so the users cannot generate a fake block,
whereas miners in the blockchain must verify any transaction.
6.6. 51% Attack
In general, an attack known as 51% attack can be executed on blockchain networks, particularly
those using proof-of-work (PoW) consensus algorithms. In this attack, an attacker acquires
control of more than 50% of the network's computing power or hash rate, enabling them to
manipulate the blockchain ledger. As a result, they can double-spend cryptocurrency, prevent
new transactions from being confirmed, or even reverse previously confirmed transactions. The
name "51% attack" is derived from the fact that the attacker must have at least 51% of the
network's computing power to carry out the attack successfully.
Defence: To prevent 51% attacks in our blockchain network, we use alternative consensus
algorithms named PBFT which create high resilience against this attack.
6.7. Distributed DOS (DDOS) Attack
This is a distributed version of the Denial of Service (DOS) Attack.
Defence: A valid node can send a limited number of transactions in the network. As a defence,
when the blockchain network receives a transaction, miners check that received transaction has
produced by a valid node then accept it.

47
Table 3. Security Analysis Against Attacks
Attack Definition Defence Resilience
Denial of
Service
(DOS)
Attack
Attacker generates a
large number of
transactions to increase
trafﬁc in the network
and disrupt the
blockchain.
Only two types of transactions can be
sent in the network. Also, each node
can send a limited number of
transactions, and the blockchain
network will reject the rest of the
user's transactions after receiving a
few messages from a specific node.
High
Modification
Attack
Attacker modifies or
removes the stored
patients' data (like
access policies and
hash) on the
blockchain ledger.
Blockchain uses an immutable ledger. High
Public
blockchain
Modification
Attacker advertises a
false ledger of blocks
and makes it as the
longest ledger.
We used PBFT consensus method to
prevent it. Also, we can use the private
type of blockchain, so the nodes are
from outside the organization cannot
work as miners to create a malicious
block.
High
Storage
Attack
Attacker wants to
remove, change, or add
data in the Off-chain
storage.
On blockchain ledger exist a hash of
the encrypted data stored in the Off-
chain storage; therefore, changes in
the data can easily be detected.
High
Appending
Attack
Attacker compromises
a miner and generates
blocks with fake
transactions to create a
false reputation.
Due to the usage of private
blockchain, so the users cannot
generate a fake block, whereas miners
in the blockchain must verify any
transaction.
High
51% Attack
Attacker controls more
than 51% of miners
and tries to
compromise the
consensus algorithm
and generate a fake
block.
The probability of occurrence of this
attack is very low due to the usage of
private blockchain and PBFT method
for consensus.
High
Distributed
DOS
(DDOS)
Attack
This is a distributed
version of the Denial
of Service (DOS)
Attack.
A valid node can send a limited
number of transactions in the network.
After the blockchain network receives
a transaction, miners check that
received transaction has produced by a
valid node then accept it.
Moderate
7. CONCLUSION

48
Patients' healthcare data are privacy-sensitive and security-sensitive, and for managing them, we
should not trust the third-parties, where they are vulnerable to attacks and abuse. In this work, we
proposed a novel platform based on the Internet of Things (IoT) and blockchain technology,
motivated by the privacy and security challenges of patients' healthcare data in e-health. Our
proposed platform enables patients to have ownership and full control over their sensitive
healthcare data collected by their IoT wearable devices. This ownership and complete control
over patients' data are satisfied by storing access control policies in a blockchain ledger by
patients to specify who from medical staff can access patients' data. In this platform, we use off-
blockchain storage to lighten the blockchain storage. Also, we use a suitable consensus method in
the blockchain network due to the resource constraint factor of IoT.
ACKNOWLEDGEMENT
The authors are thankful to Mr. Mohammad Doost and Mr. Mohammadhadi Ahmadiashtiyani for
consulting and lengthy discussions on many of the ideas used in this paper.
REFERENCES
[1] Statista, "Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025.
[Online]. Available: https://www.statista.com/statistics/471264/iot-number-of connected-devices-
worldwide/
[2] S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain and K.-S. Kwak, "The Internet of Things for
Health Care: A Comprehensive Survey," IEEE Access, vol. 3, pp. 678 - 708, June 2015.
[3] K. Abouelmehdi, A. Beni-Hessane and H. Khaloufi, "Big healthcare data preserving security and
privacy," Journal of Big Data, December 2018.
[4] Latanya Sweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty,
Fuzziness and Knowledge-Based Systems, 10(05):557–570, 2002.
[5] Arvind Narayanan and Vitaly Shmatikov. How to break anonymity of the netﬂix prize dataset. arXiv
preprint cs/0610105, 2006.
[6] Cynthia Dwork. Differential privacy. In Automata, languages and programming, pages 1–12.
Springer, 2006.
[7] Y.-A. d. Montjoye, E. Shmueli, S. S. Wang and A. S. Pentland "Protecting the Privacy of Metadata
through SafeAnswers," PLOS ONE vol. 9, July 2014.
[8] A.Sahai and B.Waters, ‘‘Fuzzy identity-based encryption,’’ in Proc.Annu. Int. Conf. Theory Appl.
Cryptograph. Techn. Berlin, Germany: Springer, 2005, pp. 457–473.
[9] J. Zhang, X. A. Wang, and J. Ma, ‘‘Data owner based attribute based encryption,’’ in Proc. Int. Conf.
Intell. Netw. Collaborative Syst. (INCOS), Sep. 2015, pp. 144–148.
[10] Craig Gentry. Fully homomorphic encryption using ideal lattices. In STOC, volume 9, pages 169–
178, 2009.
[11] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. Consulted, 1(2012):28, 2008.
[12] A. Dorri, S. S. Kanhere, R. Jurdak and P. Gauravaram, "LSB: A Lightweight Scalable BlockChain for
IoT Security and Privacy, arXiv:1712.02969 , Dec 2017.
[13] A. Kosba, A. Miller, E. Shi, Z. Wen and C. Papamanthou, "Hawk: The Blockchain Model of
Cryptography and Privacy-Preserving Smar Contracts," in 2016 IEEE Symposium on Security and
Privacy (SP), San Jose, CA, USA, August 2016.
[14] Blockchain for Financial Services. Accessed: Mar. 25, 2018. [Online]. Available:
https://www.ibm.com/blockchain/ﬁnancial-services
[15] A Decentralized Network for Internet of Things. Accessed: Mar. 25, 2018. [Online]. Available:
https://iotex.io
[16] C. Fromknecht and D. Velicanu. (2014). A Decentralized Public Key Infrastructure With Identity
Retention. [Online]. Available: https://eprint.iacr.org/2014/803.pdf
[17] Blockchain for Supply Chain. Accessed: Mar. 25, 2018. [Online]. Available:
https://www.ibm.com/blockchain/supply-chain
[18] Proof of Existence. Accessed: Mar. 25, 2018. [Online]. Available: https://proofofexistence.com

49
[19] S. Wilkinson, T. Boshevski, J. Brandoff, and V. Buterin, ‘‘Storj a peerto-peer cloud storage
network,’’ White Paper. Accessed: Mar. 25, 2018. [Online]. Available: https://storj.io/storj.pdf
[20] P. Labs. (2018). Filecoin: A Decentralized Storage Network. [Online]. Available:
https://filecoin.io/filecoin.pdf
[21] J. Benet. (2014). ‘‘IPFS-content addressed, versioned, P2P file system.’’ [Online]. Available:
https://arxiv.org/abs/1407.3561
[22] Zyskind, Guy, and Oz Nathan. "Decentralizing privacy: Using blockchain to protect personal data." In
2015 IEEE Security and Privacy Workshops, pp. 180-184. IEEE, 2015.
[23] T.-T. Kuo and L. Ohno-Machado, "ModelChain: Decentralized Privacy Preserving Healthcare
Predictive Modeling Framework on Private Blockchain Networks," arXiv:1802.01746, Feb 2018.
[24] Meisami, Sajad, Mohammad Beheshti-Atashgah, and Mohammad Reza Aref. "Using Blockchain to
Achieve Decentralized Privacy In IoT Healthcare." Cryptology ePrint Archive (2021).
[25] X. Yue, H. Wang, D. Jin, M. Li and W. Jiang, "Healthcare Data Gateways: Found Healthcare
Intelligence on Blockchain with Nove Privacy Risk Control," Journal of Medical Systems, 2016.
[26] Dwivedi, Ashutosh Dhar, Gautam Srivastava, Shalini Dhar, and Rajani Singh. "A decentralized
privacy-preserving healthcare blockchain for IoT." Sensors 19, no. 2 (2019): 326.
[27] M. Castro, B. Liskov et al., “Practical byzantine fault tolerance,” in OSDI, vol. 99, 1999, pp. 173–
186.
[28] M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli and a. M. H Rehmani, "Applications of
Blockchains in the Internet of Things: A Comprehensive Survey," IEEE Communications Society,
2018.
[29] Federal Information and Processing Standards. FIPS PUB 180-4 Secure Hash Standard ( SHS ).
(March), 2012.
[30] Daemen, Joan, and Vincent Rijmen. The design of Rijndael. Vol. 2. New York: Springer-verlag,
2002.
[31] Don Johnson, Alfred Menezes, and Scott Vanstone. The elliptic curve digital signature algorithm
(ecdsa). International Journal of Information Security, 1(1):36–63, 2001.
[32] N. Komninos, E. Philippou, and A. Pitsillides, “Survey in smart grid and smart home security: Issues,
challenges and countermeasures,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp.
1933–1954, 2014.
[33] Y. Yang and M. Ma, “Conjunctive keyword search with designated tester and timing enabled proxy
re-encryption function for e-health clouds,” IEEE Transactions on Information Forensics and
Security, vol. 11, no. 4, pp. 746–759, 2015.
[34] Q. Xia, E. Sifah, A. Smahi, S. Amofa, and X. Zhang, “Bbds:Blockchain-
baseddatasharingforelectronic medical records in cloud environments,” Information, vol. 8, no. 2, p.
44, 2017.
[35] Doost, M., Alireza Kavousi, J. Mohajeri and M. Salmasizadeh. “Analysis and Improvement of an E-
voting System Based on Blockchain.” 2020 28th Iranian Conference on Electrical Engineering
(ICEE) (2020): 1-4.
[36] Q. Xia, E. B. Sifah, K. O. Asamoah, J. Gao, X. Du, and M. Guizani, “Medshare: Trust-less medical
data sharing among cloud service providers via blockchain,” IEEE Access, vol. 5, pp. 14757–14767,
2017.
[37] Khalesi, Ali, Mahtab Mirmohseni, and Mohammad Ali Maddah-Ali. "The Capacity Region of
Distributed Multi-User Secret Sharing." arXiv preprint arXiv:2103.01568 (2021).
[38] K. Peterson, R. Deeduvanu, P. Kanjamala, and K. Boles, “A blockchain-based approach to health
information exchange networks,” in Proc. NIST Workshop Blockchain Healthcare, vol. 1, 2016, pp.
1– 10.
[39] Meisami, Sajad & Beheshti-Atashgah, Mohammad & Aref, Mohammad. (2023). Using Blockchain to
Achieve Decentralized Privacy in IoT Healthcare. International Journal on Cybernetics & Informatics.
12. 97-108. 10.5121/ijci.2023.120208.
[40] J. Zhang, N. Xue, and X. Huang, “A secure system for pervasive social network-based healthcare,”
IEEE Access, vol. 4, pp. 9239–9250, 2016.
[41] Bodell III, William E., Sajad Meisami, and Yue Duan. "Proxy Hunting: Understanding and
Characterizing Proxy-based Upgradeable Smart Contracts in Blockchains.".
[42] A. Zhang and X. Lin, “Towards secure and privacy-preserving data sharing in e-health systems via
consortium blockchain,” Journal of medical systems, vol. 42, no. 8, p. 140, 2018.

50
[43] Meisami, Sajad, Mohammad Beheshti-Atashgah, and Mohammad Reza Aref. "Using blockchain to
achieve decentralized privacy in IoT healthcare." arXiv preprint arXiv:2109.14812 (2021).
[44] Aghabagherloo, Alireza, Javad Mohajeri, Mahmoud Salmasizadeh, and Mahmood Mohassel Feghhi.
"An Efficient Anonymous Authentication Scheme Using Registration List in VANETs." In 2020 28th
Iranian Conference on Electrical Engineering (ICEE), pp. 1-5. IEEE, 2020.
[45] Duan, Yue, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, and Mu Zhang. "Towards
Automated Safety Vetting of Smart Contracts in Decentralized Applications." In Proceedings of the
2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 921-935. 2022.
AUTHORS
Sajad Meisami is PhD candidate in Computer Science at Illinois Institute of
Technology, USA. Before that, he received his M.Sc. degree from Sharif University of
Technology in Iran. His research interests include System security, Blockchain, Smart
contracts and Data security and privacy.
Sadaf Meisami is Researcher at ISSL lab at department of Electrical Engineering at
Sharif University of Technology. She received her B.Sc. in Insurance Management at
Management department of Kharazmi University. Her research interests include
Healthcare data management and Insurance management.
Melina Yousefi is Researcher at Industrial Engineering department at Isfahan
University of Technology. She received her B.Sc. in Industrial Engineering department
from this university. Her research interests include data management and Mathematical
Finance.
Mohammad Reza Aref is a professor in the department of Electrical Engineering at
Sharif University of Technology. He received his PhD degree in Electrical Engineering
from Stanford University, USA. His research interests include Information Theory,
Cryptography theory, Statistical signal processing and Communication theory. He is a
member of the IEEE.

COMBINING BLOCKCHAIN AND IOT FOR DECENTRALIZED HEALTHCARE DATA MANAGEMENT

More Related Content

Similar to COMBINING BLOCKCHAIN AND IOT FOR DECENTRALIZED HEALTHCARE DATA MANAGEMENT (20)

More from ijcisjournal2 (11)

Recently uploaded (20)

COMBINING BLOCKCHAIN AND IOT FOR DECENTRALIZED HEALTHCARE DATA MANAGEMENT