Blockchain Enforced Attribute based Access Control with ZKP for Healthcare Service

International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.3, May 2024
DOI: 10.5121/ijcnc.2024.16308 117
BLOCKCHAIN ENFORCED ATTRIBUTE BASED
ACCESS CONTROL WITH ZKP FOR
HEALTHCARE SERVICE
Dongju Lee1
and Hyunsung Kim1,2
1
Department of Computer Engineering, Kyungil University, Korea
2
Department of Mathematical Sciences, University of Malawi, Malawi
ABSTRACT
The relationship between doctors and patients is reinforced through the expanded communication channels
provided by remote healthcare services, resulting in heightened patient satisfaction and loyalty.
Nonetheless, the growth of these services is hampered by security and privacy challenges they confront.
Additionally, patient electronic health records (EHR) information is dispersed across multiple hospitals in
different formats, undermining data sovereignty. It allows any service to assert authority over their EHR,
effectively controlling its usage. This paper proposes a blockchain enforced attribute-based access control
in healthcare service. To enhance the privacy and data-sovereignty, the proposed system employs
attribute-based access control, zero-knowledge proof (ZKP) and blockchain. The role of data within our
system is pivotal in defining attributes. These attributes, in turn, form the fundamental basis for access
control criteria. Blockchain is used to keep hospital information in public chain but EHR related data in
private chain. Furthermore, EHR provides access control by using the attributed based cryptosystem
before they are stored in the blockchain. Analysis shows that the proposed system provides data
sovereignty with privacy provision based on the attributed based access control.
KEYWORDS
Healthcare service, Blockchain, Access control, Authentication, Non-interactive zero-knowledge proof.
1. INTRODUCTION
The evolution of high-speed Internet and sensor technology has made it possible for remote
healthcare services to effectively manage healthcare needs from any location, at any time [1-3].
With the development of information communication technology, healthcare is transitioning from
traditional hospital-centric care to patient-centric remote treatment, focusing on improving
convenience and accessibility. Through smart healthcare services, patients' health status can be
monitored in real-time, offering advantages in terms of time efficiency and enhancing their
quality of life. Patients appreciate the efficiency of accessing healthcare services, irrespective of
where they are located. This flexibility eliminates the constraints of time and space, enabling
direct consultations with their attending physician.
Nevertheless, safeguarding data privacy and security is crucial during data collection and
transmission in healthcare services, given their susceptibility to diverse attacks [4-10]. Successful
attacks by malicious actors could result in unintended actions through wireless body area
networks (WBAN) or Internet of things (IoT), posing life-threatening risks to patients.
Consequently, the development of data privacy and security mechanisms becomes imperative for
ensuring the safety of healthcare applications.

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In recent times, there has been significant progress in the utilization of blockchain technology,
with broader implications across diverse sectors like healthcare, WBAN, and IoT [11-15].
Preventing unauthorized data tampering is a key outcome, enhancing both system integrity and
immutability. Furthermore, it could decentralize the security and privacy requirements. A
decentralized blockchain-based authentication system for IoT was put forth by Hammi et al.,
suggesting innovative approaches to security [13]. Khashan & Khafajah introduced an
authentication architecture for heterogeneous IoT, blending both centralized and blockchain-
based elements [14]. They argued that their architecture provides authentication, secure identity
management, data integrity, data freshness, key refreshment and non-repudiation. Liu et al.
proposed a blockchain enforced privacy preserving authentication and key agreement and access
control (BP-AKAA) for industrial IoT [15].
While medical information exchanged between patients and doctors is typically perceived as
patient-owned and managed, it is often stored and managed within the hospital's database [16-
18]. Accessing such information requires patients to visit the hospital in person, and even then,
access is often restricted. This limitation on accessing one's information diminishes their right to
self-determination. Moreover, integrating information becomes challenging when patients see
multiple doctors across various hospitals. Since medical data is hospital-dependent and centrally
managed, any security breach compromises the patient's electronic health records, leaving them
reliant solely on the hospital's data management [19-21]. To cope with the centralized problem,
Chen et al. proposed a medical data-sharing mechanism based on attribute-based access control
and privacy protection [22]. They used the K-anonymity and searchable encryption techniques
for security and privacy reasons. However, it provides a detailed attribute-based access control
yet requires a secure channel for the registration of the participants. Azbeg et al. proposed a
healthcare system that integrates IoT with blockchain named BlockMedCare [23]. Within
BlockMedCare, security is established through the utilization of a re-encryption proxy in
conjunction with blockchain, ensuring the safe storage of hash data. However, it does not
consider data sovereignty, which involves the rights and obligations regarding the ownership,
control, and access to data [24]. Data sovereignty is an emphasis on ensuring that data remains
within the jurisdiction and control of the entity that owns it. This concept becomes particularly
relevant in cross-border data transfers, where data may move across different legal jurisdictions,
raising concerns about compliance with local regulations, privacy laws, and security standards
[25]. As observed in the analysis of relevant research, suggestions have been made for
decentralized environments in healthcare or security techniques utilizing ZKP. However, secure
access control methods in decentralized environments ensuring data sovereignty have yet to be
explored.
The purpose of this paper is to propose a blockchain-enforced attribute-based access control in
healthcare services for the decentralized security and privacy and data sovereignty. The proposed
system employs attribute-based access control, ZKP and blockchain. The definition of attribute
within our system can be determined by considering the role of data, which serves as the
foundational criterion for access control. Blockchain is used to keep hospital information in the
public chain but EHR related data in the private chain. Furthermore, EHR provides access control
by using the attributed based cryptosystem before they are stored in the blockchain. The main
contributions of this paper are as follows:
- Attribute-based access control with blockchain is proposed to provide data sovereignty of
EHR for healthcare services. This method is both time-efficient and energy-saving, aligning
perfectly with the limited resources of IoT devices. By doing so, it is possible to reduce
misuse of patient data and ensure data sovereignty.

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- This paper effectively devises a new authentication scheme based on ZKP and blockchain.
Through this functionality, system participants can register using the Internet, guaranteeing
secure communication and data exchange across all connected hospitals.
- The blockchain keeps an access control list (ACL) and logs for the patient’s EHR-related
data. By providing a specific definition of access control details in ACL, it is possible to
guarantee patient sovereignty over their information, and through detailed log management,
patients can verify how their data is being utilized.
- A doctor collaboration scheme is additionally devised for the patient to freely visit any
second hospital and to be treated for the healthcare service securely. However, the patient
does not need to consider their EHR data but the main doctor could provide a delegation
service to the second hospital doctor.
- The performance and security analyses are presented. Through our approach, the results and
comparisons with related schemes demonstrate a substantial potential to enhance patient data
sovereignty and privacy. Furthermore, the results illustrate the resilience of our security
system, showcasing its ability to withstand attacks and meet the security demands inherent in
IoT systems.
The paper is organized as follows: The relevant existing security primitives to understand this
paper are presented with the related works in Section 2. The proposed security system with
related phases is explained in Section 3. Section 4 provides performance and security analysis
with proper comparisons among related works. Section 5 concludes the research.
2. PRELIMINARY AND RELATED WORKS
In this section, a succinct explanation is given concerning the cryptographic primitives utilized in
the context of this paper. Furthermore, we provide a detailed analysis of some related works,
which are used for the comparisons of analysis.
2.1. Blockchain
The concept of blockchain involves creating a distributed ledger where data blocks are organized
into a chain format, following a strict chronological order. [26]. This introduces a fresh trust
paradigm within the open network, allowing system participants to establish trust even in
decentralized settings. In blockchain systems, the security of the ledger relies on the
interconnected structure of hash values and the consensus algorithm. The hash value of the
previous block header is included in the latest block. As a result of this synchronized updating
process, any attempt to effect unauthorized changes within the blockchain network faces
significant barriers. Specifically, without controlling more than 51% of the total computational
power of the system, adversaries are unable to execute alterations effectively. This inherent
security feature underscores the robustness and resilience of blockchain technology against
malicious attacks. Ethereum, HyperLedger Fabric, and Corda R3 are among the diverse platforms
available. Within the healthcare environment leveraging blockchain technology, it's crucial to
offer varying levels of control to system participants. This is only possible with permission
frameworks like HyperLedger Fabric or Corda. In contrast to Ethereum, both Fabric and Corda
offer more detailed access control, allowing participants to have their permissions tailored to
reading, creating, updating, and deleting rights, thereby enhancing privacy protection. Within this
study, HyperLedger Fabric was employed as the chosen blockchain platform. Fabric introduces a
novel blockchain architecture with a focus on enhancing resiliency, flexibility, scalability, and
confidentiality. [27]. Within a public blockchain system, individuals are able to participate freely,
without any requirement for a specific identity. Conversely, a private blockchain restricts access

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to only identified participants. Through this method, communication is restricted to trusted
participants, promoting a secure mode of interaction.
2.2. Zero-Knowledge Proof
Goldwasser et al. proposed the concept of ZKP [28]. ZKP enables privacy-preserving
authentication. In this paper, we will use one discrete logarithm-based ZKP to realize
certificateless key generation and privacy protected authentication which was used in [23] as
Definition 1.
Definition 1. Proof of knowledge of a discrete logarithm (PoK).
Within the given public parameters (G, g, p, H()), G denotes a multiplicative cyclic group
characterized by a prime order p, with g acting as a generator of G, and H() representing a
cryptographically secure one-way hash function. For Y∈G, a representation of Y in relation to g
involves an element x∈Zp, which satisfies the relation R={(x, Y)∈Zp × G: gx
=Y}. The prover P
endeavors to persuade a skeptical yet honest verifier ⱱ that he (or she) possesses knowledge of a
representation of a given Y, all the while safeguarding the secrecy of the underlying secret x.
- P chooses v ← Zp
*
, R ← {0,1}*
to compute V=gv
, c=H(g, Y, V) and y = v - cx(mod p). P sends the
proof ΨPoK = <Y, V, r> to the verifier.
- ⱱ computes c first. If the condition V = gr
∙Yc
holds, ⱱ accepts this proof, otherwise rejects.
2.3. Attribute-based Data Encryption
Waters introduced a ciphertext-policy attribute-based encryption scheme that is both expressive
and efficient, providing provable security. This scheme comprises the following algorithms [29]:
- 𝑆𝑒𝑡𝑢𝑝(𝜆, U) → (MPK, MSK): a central authorization entity utilizes a security parameter 𝜆 and
an attribute universe U as input, executing the algorithm to generate the system’s public and
private key (MPK, MSK).
- 𝐸MPK(MSG) → CT: the encryption algorithm requires the message MSG to be encrypted and the
system’s public key, which incorporates an attribute access structure, as input. It then produces
ciphertext CT, ensuring that only a user whose attribute set meets the access structure criteria can
successfully decrypt it.
- 𝐾𝑒𝑦𝐺𝑒𝑛(MPK, MSK, S) → DK: the key generation algorithm requires the system’s public and
private keys along with a user attribute set S, as input. It then generates a decryption (private) key
DK for the user.
- 𝐷MPK(CT) → MSG: the decryption algorithm requires a ciphertext related to an access structure
and the system key, which corresponds to a set of attributes, as input. If the attribute set meets the
access structure, the algorithm will produce valid plaintext MSG.
2.4. Attribute-based Access Control
Attribute-based encryption is an encryption technique in which only a user having an attribute
value suitable for the encrypted data may decrypt data. Hu et al. defined a high-level ABAC as
follows [30]:

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- A logical access control methodology where authorization to perform a set of operations is
determined by evaluating attributes associated with the subject, object, quested operations, and,
in some cases, environment conditions against policy, rules, or relationships that describe the
allowable operations for a given set of attributes.
- Attributes are characteristics that define specific aspects of the subject, object, environmental
conditions, and/or requested actions predefined and preassigned by an authority. Attributes
typically consist of three components: an optional category that denotes the type of information
conveyed by the attribute, a name, and a value.
- A subject is generally an individual, process, or device that is responsible for actively
transmitting information between objects or initiating changes in the system’s state. This entity
has the potential to represent either the user, the requester, or a mechanism acting in the interest
of either the user or the requester. A subject within a system can encompass non-human entities
like systems or processes, not necessarily limited to human actors. Typically, subjects undertake
actions representing a particular individual or organization. Subjects have the potential to be
assigned attributes that detail various aspects such as their name, organization affiliation,
citizenship, etc.
- An object is an inert entity within the information system framework, encompassing devices,
files, records, tables, processes, programs, networks, and domains, which either contain or
receive information. When a subject gains access to an object, it inherently means gaining access
to the information stored within it. This object can encompass various entities, including
resources or requested entities, as well as anything that a subject may interact with, such as data,
applications, services, devices, and networks.
- An operation involves the execution of a function in response to a subject’s request on an object
within the system. The range of operations encompasses actions such as read, write, edit, delete,
author, copy, execute, and modify.
- Policy is the representation of rules or relationships that define the set of allowable operations a
subject may perform upon an object in permitted environment conditions.
2.5. Related Works
This subsection aims to examine works in the realm of IoT or healthcare that have implemented
blockchain technology to establish decentralized security architecture and offer access control
[13-15, 23]. These works are utilized for comparison with the proposed system in the analysis
section.
To decentralize the authentication system, Hammi et al. proposed a decentralized system called
bubbles of trust, which plans to ensure a robust identification and authentication of devices [13].
Utilizing blockchains, their system establishes secure virtual zones wherein entities can mutually
identify and trust one another. It provides a good design concept for the decentralization of
security and privacy systems. However, it does not provide any cross-domain security concept
nor data sovereignty and access control.
For the heterogeneous and scalable IoT systems, Khashan & Khafajah proposed a hybrid
centralized and blockchain-based authentication architecture for heterogeneous IoT systems
based on a lightweight cryptographic methods [14]. They argued that centralized authentication
schemes is inappropriate for cross-domain authentication and limit the scalability of IoT
networks. So, edge servers were deployed to provide centralized authentication based on
blockchain networks in their architecture. They argued that their architecture provides
authentication, secure identity management, data integrity, data freshness, key refreshment and
non-repudiation and is strong against various attacks. However, their architecture does not
provide any details on the data management for the system and thereby it does not consider any
data sovereignty.

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Liu et al. proposed a blockchain-enforced privacy-preserving authentication and key agreement
and access control (BP-AKAA) for industrial IoT [15]. It is purposed to solve trust issues
between mutually untrusted subnets through third-party trusted servers. BP-AKAA is based on
attribute-based access control, non-interactive ZKP and blockchain for device to device
communication security. They argued that BP-AKAA solved the untrust issue of cross-domain
authentication with the assistance of distributed blockchain. Despite its advantages, BP-AKAA
lacks data sovereignty for network participants since the encrypted data cannot be controlled by
its owner. Additionally, it fails to offer comprehensive insights into attribute usage for secure
data management.
Azbeg et al. introduced BlockMedCare, a healthcare system combining IoT and blockchain
technologies, designed to facilitate remote patient monitoring. This system aims to address the
needs of patients with chronic diseases that necessitate ongoing supervision. [23].
BlockMedCare's security framework relies on a combination of re-encryption proxy and
blockchain technology, facilitating the storage of hash data. To address blockchain scalability
concerns, the implementation incorporated an off-chain database utilizing the InterPlanetary File
System (IPFS) for data storage. As a use case, they applied BlockMedCare to diabetes
management and showed the execution results with good security and performance aspects.
However, BlockMedCare does not consider doctor collaborations between different hospitals or
data sovereignty.
Although various researches have been conducted, there has been no researches that can
guarantee open channel registration, data sovereignty, decentralized security framework and
cross-domain collaboration framework for healthcare applications.
3. BLOCKCHAIN-ENFORCED ATTRIBUTE- BASED ACCESS CONTROL
In this section, a blockchain-enforced attribute-based access control in healthcare service is
proposed. It uses blockchain, ZKP, and attribute-based access control to protect the
authentication privacy for healthcare services. Blockchain keeps some healthcare service
information for decentralization purposes. It keeps hospital information and public key
information in the public chain but EHR related data, ACL and log data in private chain, which
could be further controlled based on the access control scheme.
Figure 1. Overview of the proposed security system

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3.1. System Model
Figure 1 depicts the system model for attribute-based access control enforced by blockchain. The
configuration relies on a blockchain data structure comprising both public and private chains. The
designated roles for each entity are defined as follows:
(1) Hospital server (HS): HS acts as the central nexus for remote healthcare services,
functioning as the focal point for all aspects of patient care. It is responsible for the
registration of system components and oversees the coordination between patients and
medical staff. The objective is to provide secure and privacy-assured remote healthcare
services by collaborating with in-hospital AS.
(2)Attribute server (AS): AS serves as the key generation center responsible for all processes
related to keys, especially attribute keys. It collaborates with HS to generate keys, publish
public key information on the blockchain, and securely transmit private key information to
system participants through HS.
(3) Patient: The entity is the subject of remote healthcare services. At intervals determined by
the doctor for remote healthcare services, EHR is stored on the blockchain using attribute
keys.
(4) Doctors and nurses: They offer remote medical consultation services to patients located at a
distance. The access scope of EHR may be limited based on system access permissions. It
verifies the health information of patients stored on the blockchain and conducts appropriate
remote consultations.
(5) Blockchain: Public blockchain stores hospital details and public key information for the
system configuration. Private blockchain keeps patient’s EHR, ACL and logs data on them.
Table 1. Notations
Notation Description
k
MPKAS, ASKAS
RPKAS, RSKAS
ISKX, IPKX
AUKX
IDX, PWX
KDF()
ΨPoK
g
G, GT
H1, H2
C
TS
e()
H1()
H2()
EX()
||

A security parameter
The master public key, the attribute secret key
The public and private key of AS
Private identity key and public identity key of
X
Attribute key of X
Identifier and password of X
A key derivation function
A ZKP of knowledge of a discrete algorithm
A generator of G
Two multiplicative groups of prime order p
Secure hash functions
Cipher text
Timestamp
Bilinear pairing
A hash function mapping {0, 1}*
 G
A hash function mapping {0, 1}*
 Zp
*
Attribute-based data encryption with the key X
String concatenation operation
Point multiplication operation
(6)Main hospital and collaboration hospital: Main hospital provides remote medical
consultation services for patients. However, in cases where collaborative treatment with a
doctor from another hospital, which is a cross-domain situation, is required for the patient, it

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addresses such requirements by coordinating with a collaboration hospital using the cross-
domain collaboration phase.
The data structure of the proposed system is formulated upon hybrid principles of both public and
private blockchain technology. In our system, there exist five ledgers tasked with storing hospital
particulars, public key data, patient EHR, ACL, and logs. The initial two ledgers are designated
for the public blockchain, while the remaining three serve the private blockchain. The data logs
are typically public under most circumstances. Nevertheless, logs pertaining to access to EHR
may be considered confidential in certain instances, especially focused on healthcare
applications. Consequently, we store such logs within the private blockchain. Public blockchains
store information intended for sharing among all system participants. This information requires
integrity assurance and is organized to facilitate easy verification by system participants.
- Hospital particulars ledger: Various information about hospitals that needs to be shared with
patients is stored. Specifically, it provides detailed information about physicians and maintains
information about collaborating hospitals and physicians. This allows patients to conveniently
select their primary care doctors even from remote locations and make informed decisions about
collaboration.
- Public key data ledger: This ledger stores public key information required for initialization and
registration. It aims to maintain cryptographic shared information for ensuring secure healthcare
services. Only participants registered on the hospital server can generate relevant data but
everybody could access the information.
Within the private blockchain, access is restricted solely to entities possessing requisite attributes,
as authenticated by the blockchain. This private ledger securely maintains EHRs, ACLs and logs
ensuring privacy and security through suitable protective measures. Further elucidation of the
configuration of ledgers is provided below:
- EHR ledger: EHRs represent standard personal private data, affording individuals complete
ownership rights. EHRs are encrypted by attribute-based encryption as mentioned in EHR
generation and retrieval phase, which not only can protect data privacy but also can improve the
efficiency of data access. Only authorized users with the proper attributes can access this ledger
by smart contracts. The sharing of EHRs is guaranteed even if patients are incapacitated.
- ACL ledger: Management of comprehensive details concerning patient EHR ledger access is
conducted. All data presented in this ledger is under the patient's right to ensure data sovereignty.
Particularly, even in instances necessitating cross-domain collaboration medical care, the primary
doctor can introduce new ACLs with the patient's authorization only.
- Logs ledger: All records related to the patient's EHR ledger are stored. By regularly checking
this ledger, patients can determine whether there has been any infringement on their data
sovereignty regarding their EHR data.
This system considers the practical Byzantine fault tolerance (PBFT) algorithm for the consensus
used in the public blockchain. Nonetheless, the system offers flexibility through pluggable
consensus mechanisms, allowing for the integration of alternative algorithms like Raft or Kafka
to align with the specific needs of the healthcare application. To simplify matters, in the proposed
system, HS holds the authority to make decisions regarding all transactions within the private
blockchain. The notations used in this paper are summarized in Table 1.
3.2. System Procedure
The proposed system has four phases: system initialization, registration, authentication, EHR
generation and retrieval and cross-domain collaboration.

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3.2.1. System Initialization
HS sends a system security initialization request to AS. AS chooses a security parameter k ∈ N
and generates two multiplicative cyclic groups G and GT of a prime order p. Let g be a generator
of G, and e(): GT = G × G be a bilinear map. Let D = (G, GT, g, p, e()) be a bilinear group. AS
selects two one-way hash functions: H1(): {0, 1}*
 G and H2(): {0, 1}*
 Zp
*
. A key
derivation function (KDF) is selected for generating symmetric encryption keys. AS chooses its
private key, RSKAS=α ← Zp
*
and proceeds to calculate the public key RPKAS=gα
. AS selects ti ←
Zp
*
for each attribute ai ∈ UA to compute attribute public keys Ti={gti
}, where UA is the attribute
universe with N attributes. AS releases the master public key MPKAS=(D, H1, H2, KDF(), RPK)
onto the blockchain for public access, while safeguarding the attribute secret key ASKAS= ({ti}i∈[1,
N], RSKAS) within a trusted platform module (TPM) to maintain confidentiality. After publishing
MPKAS, AS informs HS to proceed with the next process.
Figure 2. System initialization phase
HS gets MPK, chooses a random number ISKHS=β, which is an identifier private key, and
computes its incomplete public key IPKHS’=gβ
. Then, it constructs a zero-knowledge proof (ZKP),
ΨPoK = <IPKHS’, u, ξ>, of its private identifier key as follows: HS selects μ ← Zp to compute u =
gμ
, c = H2(g||IPKHS’||u) and ξ = μ – cβ (mod p). Then, HS sends ΨPoK to AS. AS verifies the ZKP
as follows: AS computes c = H2(g||IPKHS’||u) and checks if u = gξ
(IPKHS’)c
. If that passes, AS
issues a complete public key IPKHS=(gβ’
)γ’
with a newly selected random number γ’. Then, AS
sends the public key back to HS. Figure 2 shows the process.
3.2.2. Registration
[Patient Registration] This process is conducted between the patient and HS. First of all, a patient
generates an identifier IDPT and their private identifier key ISKPT = λ = H2(IDPT||PWPT) with a
password PWPT. Then, he (or she) computes an incomplete public identifier key IPKPT’=gλ
. He
(or she) constructs a zero-knowledge proof ΨPoK_P = <IPKPT’, U, ξ> of his (or her) private
identifier key as follows: he (or she) selects σ ← Zp
*
to compute U=gσ
, c=H2(g||IPKPT’||U) and
ξ=σ–cλ (mod p). After that, he (or she) sends ΨPoK_P to HS for registration. After HS receives
ΨPoK_P, HS sends it to AS. AS verifies this proof as follows: it computes c’ = H2(g||IPKPT’||U) and
checks if U is equal to gξ
(IPKPT’)c
. If that passes, AS chooses l ← Zp
*
, computes IPKPT =(gλ
)l
,
stores IPKPT in the public blockchain and sends (IPKPT, IPKPT’) to patient via HS.
[Doctors and Nurses Registration] First of all, doctor (or nurse) generates an identifier IDDO and a
private identifier key ISKDO = φ = H2(IDDO||PWDO) with a password PWDO. Then, he (or she)
computes an incomplete public identifier key IPKDO’ = gφ
. He (or she) constructs a ZKP ΨPoK_D =
<IPKDO’, V, r> of his (or her) private identifier key and sends it to HS as follows: He (or she)

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selects v ← Zp
*
to compute V = gv
, c = H2(g||IPKDO’||V) and r = v – cφ (mod p). After that, he (or
she) sends ΨPoK_D to HS. HS sends it to AS for verification. AS verifies this proof as follows: it
computes c = H2(g||IPKDO’||V) and checks if V is equal to gr
(IPKDO’)c
. If that passes, AS
computes IPKDO = (gv
)ω
with ω ← Zp
*
, stores IPKDO in the public blockchain and sends (IPKDO,
IPKDO’) to doctors (or nurses) via HS.
3.2.3. Authentication
Authentication is used between patient and doctor (or nurse) for the rich hospital service except
EHR check. HS with blockchain works as a central credential check between two entities.
First of all, patient constructs a ZKP signature ΨPoK_A on a self-selected random number r ← Zp
*
and R = gr
and sends this proof with a medical service request to the doctor as follows: Let req be
the patient’s medical service request message and TSPT be a timestamp of patient. Patient
encrypts req using his (or her) AUKPT (attribute key) as Creq = EAUKPT(req). Patient computes f =
H1(TSPT||Creq||R), p1 = f r
and Z = f λ
with the secret identifier key λ. Patient computes c =
H2(f||AUKPT||Z) and y = r + cλ (mod p) and sends ΨPoK_A = <IPKPT, Z, y> along with {TSPT, Creq, R,
p1} to the doctor. After doctor receives ΨPoK_A, doctor sends it for verifying the proof to HS. Then,
HS verifies this proof as follows: it computes f’=H1(TSPT||Creq||R) and c’ = H2(f||AUKPT||Z) with
the blockchain-stored keys of the patient AUKPT and IPKPT. After that, it checks if the following
equation holds f’y
= p1Zc’
. If the validation check holds, it sends an acknowledgement Ack ∈ G to
doctor. Then, doctor accesses to blockchain which stored AUKPT and patient’s medical
information. Using AUKPT, doctor checks req’ by decrypting Creq as req’ = 𝐷AUKDO(Creq). After
confirming the request, doctor generates an appropriate response message Rep on the patient’s
request based on the information stored in blockchain. Finally, doctor sends Rep to the patient.
Figure 3. Authentication phase
3.2.4. EHR Generation and Retrieval
Patients should store EHR data periodically to blockchain and doctors should check the
information. The proposed system uses blockchain to store the patients’ EHR data. Attribute-
based encryption with access control is used to protect patient’s data. There should be many
required data fields for a patient to present their medical information to doctors. However, we
will just simplify the data as EHR only for the patient. The patient's EHR must be stored in a
private blockchain, which should not leak any privacy- related information to anybody without
their privilege. For this, the proposed system uses an attribute-based encryption with AUKPT. The
patient computes CEHR = EAUKPT(EHR) and stores it in the blockchain. Only the privileged entity
with proper attribute key could access the EHR but not the others.

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Whenever doctor needs to check any patient’s medical status in remote environment, he (or she)
needs to access the blockchain to reach EHR data to check patient’s health status, which requires
privilege to access the contents of patient’s encrypted data CEHR. Doctor uses his (or her) attribute
key, AUKDO to decrypt the encrypted data CEHR as EHR’ = 𝐷AUKDO(CEHR).
3.2.5. Cross-Domain Collaboration
In case where collaborative treatment with a doctor from another hospital, which is a cross-
domain situation, is required for the patient, it addresses such requirements by coordinating with
a collaboration hospital using the cross-domain collaboration phase.
The phase is to give EHR data access rights to the collaboration doctor in different hospital
domain from a main hospital doctors via a patient’s request. So, main hospital doctor should
write a proper request to the collaboration doctor in a different hospital or the same hospital only
if any patient’s request their healthcare service. If the main hospital sends a verification method
with a request for medical cooperation to the collaboration hospital, the collaboration hospital
will verify it via HS and approve or refuse the request for medical cooperation as follows: First,
doctor (or nurse) of main hospital constructs a ZKP signature ΨPoE on a self-selected random
number r ← Zp
*
and R = gr
using its secret identifier key and sends it with a medical cooperation
request to the doctor of collaboration hospital as follows: Let MD be a main hospital’s doctor,
CD be a collaboration hospital’s doctor and TSDO be a timestamp of MD. MD encrypts req, which
should clearly be mentioned on the patient’s detailed information, data access right clarification
by considering the attribute, and the allowed time period of data access, using his (or her) AUKMD
as Creq = EAUKMD(req). MD computes f = H1(TSDO||Creq||R), p1 = f r
and Z = f λ
with the secret
identifier key λ. MD computes c = H2(f||AUKMD||Z) and y = r + cλ (mod p) and sends ΨPoK_A =
<IPKMD, Z, y> along with {TSDO, Creq, R, p1} to CD. Then, CD sends it to HS for the verification
and establishes a temporal ACL to access the patient’s EHR information. HS verification process
is the same as the authentication phase. Only if the verification is successful, HS sets up ACL of
CD for the patient’s data access right for the proper time periods mentioned in the req. CD can
access the patient’s EHR data freely as the method mentioned in the EHR retrieval phase.
4. IMPLEMENTATION
Instead of utilizing actual systems and physical hardware, this methodology simulates the
interaction among diverse components, without necessarily replicating the entire network stack.
This creates an entirely controlled and reproducible environment for conducting experiments. As
there is no direct reliance on hardware or genuine networks, adjusting the scale of the network by
modifying parameters such as the total number of transactions, rate control, virtual machine
count, block size, number of rounds, etc., becomes more feasible. Various software platforms are
available for assessing Blockchain among which we have employed Hyperledger fabric.
Table 2 shows the requirements and specifications for the functionality implementation of the
proposed system. Five numbers of virtual machines have been deployed to put system entities
roles in our implementation. They are for HS, AS, patient and two doctors. PBFT is used for the
public blockchain consensus and virtual machine 1 works as the authority for the private
blockchain.

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Table 2. Requirements and specification for the implementation
Requirements Specification
Operating system
Virtual machine 1
Virtual machine 2
Virtual machine 3
Virtual machine 4
Virtual machine 5
cURL tool
Javascript
Node JS
NPM
VS code
Hypherledger fabric
Ubuntu Linux 18.04(64bits)
Ubuntu Linux 18.04(8GB RAM, 64bits)
Version 8.7.1
1.8.5
Version 16.13.2
Version 8.1.2
Version 1.85
2.0.1
Figure 4. Parts of AS code
Figure 4. Part of EHR generation and retrieval
In this simulation, we tested secure registration via patient's ZKP on the open channel and
confirmed blockchain transactions ensuring EHR security through attribute-based encryption. We
verified EHR validation through the main doctor (virtual machine 4) and tested the process for

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collaborative patient care with the collaborating doctor (virtual machine 5). Throughout these
processes, we validated the proper generation of log records for accessing patient EHR-related
information. Figures 3 and 4 shows parts of codes for the proposed system.
5. SECURITY AND PERFORMANCE ANALYSIS
The focus of this section is to conduct a thorough analysis of the proposed system, assessing both
its security and performance while making appropriate comparisons. We introduce an attack
model and security analysis focused on the presence of attacks.
5.1. Security Analysis
This subsection provides security analysis based on the required security features and attacks for
the healthcare applications after introducing attack model. Table 3 shows comparisons of security
perspectives between related works.
Table 3. Security feature comparison
Feature [13] [14] [15] [23] Proposed
Cryptography
Authentication
type
Data
sovereignty
Data integrity
Non-
repudiation
Data freshness
Attack
resistance
Asymmetric
No
Decentralized
blockchain
based
No
Yes
Yes
Yes
Moderate
Symmetric &
Asymmetric
Decentralized
blockchain
based
No
Yes
Yes
Yes
Strong
Asymmetric
Decentralized
blockchain
based
No
Yes
Yes
Yes
Strong
Symmetric &
Asymmetric
Decentralized
blockchain
based
No
Yes
Yes
Yes
Moderate
Symmetric &
Asymmetric
Decentralized
blockchain
based
Yes
Yes
Yes
Yes
Strong
[13] Hammai et al., [14] Khashan & Khafajah, [15] Liu et al., [23] Azbeg et al.
5.1.1. Attack Model
We adopt the Dolev-Yao threat model that has the assumption that the communicating entities
are not fully trustworthy and data sharing is performed over insecure public channels [31].
Furthermore, we consider the following powers adversaries have:
- An attacker can control any internet connection between parties.
- There exists a safe stage in a which security module can be computed in the absence of attacks.
- An attacker cannot control all the behavioral models associated with patient’s device.
There is no supplemental knowledge an attacker can obtain from physically accessing network
participants, preserving the confidentiality of the system's information.
- The blockchain technology utilized for constructing the public ledger adheres to standard
security requirements already established for conventional blockchain applications.
- The cryptographic hash function chosen demonstrates resilience against collision, preimage, and
second preimage attacks, ensuring robust security measures.

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5.1.2. Data Sovereignty
To access data stored in the proposed system, any entity should have access rights in ACL and
could be the classified the accessibility of the data depending on their role in the system, which
was defined as an attribute. Furthermore, the main doctor could allow the accessibility to the
cross-domain doctor based on the patient’s request. In addition to that, the proposed system keeps
a log blockchain to keep track the data usage of any patient’s data. EHR data are encrypted based
on the attribute-based cryptosystem, which could be accessed to someone only has right to access
the data. Thereby, the proposed system provide data sovereignty.
To ensure data sovereignty, an analysis of unauthorized data access and safety in data breach
scenarios is conducted. An attacker with the power of attack model gains unauthorized access to
the remote healthcare application through various means, such as exploiting vulnerabilities in the
application's authentication system or stealing login credentials through phishing attacks. Using
sophisticated hacking tools, the attacker bypasses any weak encryption or access controls in place,
gaining unrestricted access to the medical system. Patients may suffer from identity theft,
financial fraud, or discrimination based on their health conditions due to the exposure of their
sensitive data. The healthcare provider faces severe reputational damage and legal consequences
for failing to protect patient information, leading to a loss of trust among patients and
stakeholders. However, the proposed system could cope with these attacks based on the robust
authentication and the attribute-based access control. Furthermore, there is no way that the
attacker could access to the contents of the encrypted EHR without having the legitimate entity’s
proper attributes. Furthermore, any access trial for the EHR could be recorded their logs in the
log ledger.
5.1.3. Data Confidentiality
By incorporating blockchain into the proposed system, each participating entity can be assigned a
unique public key, minimizing the likelihood of collisions. This capability is a significant
advantage of blockchain technology, as it eliminates the need for the costly traditional public key
infrastructure typically used for key distribution. The public key is accompanied by the attribute
key, enabling the simultaneous assurance of confidentiality and access control for EHR. This
combination can then be utilized for session key exchange, establishing a secure channel between
entities. Furthermore, the proposed security system uses an attribute-based cryptosystem to keep
data confidential and all healthcare related data are kept in the private blockchain. Thereby, the
proposed system provides data confidentiality.
Potential attack scenario for data confidentiality in the healthcare application is that an attacker
gains access to the remote healthcare application through a compromised user account or by
exploiting a vulnerability in the application's authentication system. Once inside, the attacker
employs various techniques to exfiltrate sensitive patient data stored in the blockchain. However,
the proposed system could cope with this trial with authentication mechanism and the attribute-
based data encryption applied to the EHR.
5.1.4. Data Integrity and Non-repudiation
Prevention of data modification is ensured by hashing and storing all registration and secret
construction transactions in the blockchains. Due to the utilization of tamper-proof blockchain
technology in the proposed system, every activity is maintained as immutable transaction records.
Furthermore, each transactions are recorded their logs in blockchain. It is impossible for different
entities to dispute or alter the activities or messages they have executed or transmitted. Each
message is signed using its corresponding attribute private key, which is linked to its public key.

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Consequently, the system has the capability to identify it automatically. Moreover, each
transaction is signed using the private key and mapped with its identity on the blockchain
network. Addition to that, each message should use ZKP, which has the relationship with the
entities public key stored in the public blockchain and has the difficulty of discrete logarithm as
described in Definition 1. Thereby, the system provide non-repudiation feature.
To compromise the integrity of patient data and undermine the non-repudiation mechanisms, an
attacker targets a remote healthcare application, allowing for unauthorized modifications to EHR
and denying responsibility for such actions. For that, attacker could apply for exploiting
vulnerabilities, data tampering, masquerading as authorized entity and fabricating logs. However,
the proposed system uses blcockchain architecture to cope from these attacks.
5.1.5. Data Freshness
The freshness of data guarantees that any received message is current, preventing adversaries
from reusing or replaying it. Within this context, the adversary has the capability to replay the
intercepted messages on the blockchain in the future, posing a threat of attack. In order to
safeguard the system against potential replay attacks, it is necessary to authenticate the freshness
of the message by associating it with a specific timeframe. However, blockchain systems
encounter difficulties due to a shortage of randomness, making the generation of nonce values a
challenging task. Moreover, systems relying solely on a timestamp for verification are
exceptionally susceptible to time synchronization attacks. Such vulnerabilities can result in
significant security risks, including denial of service attacks. As shown in our proposed system,
we combined timestamp with nonce values. When any entity receives a message, it first verifies
its ZKP based on them. If the knowledge of proof is not validated, the message is rejected.
Consequently, an adversary is unable to replay a past message, ensuring that the message's
freshness remains intact at all times.
To compromise the this feature, an attacker targets a remote healthcare application to
compromise the freshness of patient data, leading to outdated or inaccurate information being
used for medical decision-making and treatment. The attacker could intercepting communication,
delaying data transmission, exploiting stale data or manipulating treatment plans. To cope with
these trials, the proposed system uses session dependent nonce values in ZKP and timestamp in
blockchain.
5.1.6. Sybil Attack Prevention
Sybil attack is a type of security threat in which an individual or group creates multiple nodes,
accounts, or devices to take control or exploit a blockchain network. Remember that nodes
validate transactions on a blockchain and run consensus. Every registered entity is required to
store its public key information in a public blockchain, making it challenging for an attacker to
fabricate multiple false identities. Additionally, blockchain employs a strategy of increasing costs
to create a new identity, thus demanding a substantial investment to introduce a considerable
number of pseudonymous false nodes. Suppose an adversary intercepts a communication
message exchanged between entities and proceeds to tamper with the message by inserting
malicious code. In the proposed system, ZKP should be verified by the counterpart, which has the
difficulty of discrete logarithms in Definition 1. As a result, whether in centralized or
decentralized communication scenarios, the data flowing through the network is immune to
tampering and safeguarded against diverse attacks.
An attacker conducts a Sybil attack against a remote healthcare application to compromise the
integrity of patient data, manipulate medical records, and disrupt healthcare services. There are

132
attack trials including creation of fake identities, infiltration of patient data and manipulation of
EHRs. To mitigate the risk of Sybil attacks, the proposed system uses public key ledger to cope
from the fake identities attack, EHR ledger encrypted with the attribute-based encryption for
infiltration of patient data and manipulation of EHRs.
5.1.7. Man-in-the-Middle Attacks Prevention
Suppose an adversary is able to eavesdrop on transmitted messages and successfully retrieve the
public parameters using a man-in-the-middle attack. In the proposed system, ZKP with nonce and
timestamp is independently produced by each entity, and the data are encrypted using an attribute
key. Decrypting the ciphertext is the initial step for a node to read a message. The adversary is
unable to know the key related information nor nonce, which has a difficulty of discrete
logarithm problem. Thus, the proposed system is protected against eavesdropping and man-in-
the-middle attacks.
An attacker conducts a man-in-the-middle (MITM) attack against a remote healthcare application
to intercept and manipulate sensitive patient data exchanged between the application and its
users, such as healthcare providers and patients. They could try to intercept of communication,
spoofing legitimate communication, data manipulation, eavesdropping on sensitive information
and injection of malicious content. However, the proposed system only exchanges messages for
registration and authentication, which is secured based on ZKP. It is infeasible the attacker gain
any useful information from the communication. Furthermore, the patient’s health-related
information is not communicated over insecure channels but personally stored in the private
blockchain directly by the patient.
5.1.8. System-Level Attacks Prevention
System-level attacks target vulnerabilities inherent in the system architecture, including memory
modules, system applications, and design flaws, within healthcare systems. Exploiting these
vulnerabilities allows attackers to illicitly seize control and access sensitive data. Within the
realm of healthcare systems, two primary types of system-level attacks exist: exploits of weak
authentication schemes and privilege escalation attacks on healthcare devices.
Weak authentication schemes: Weak authentication refers to a situation where the authentication
mechanism's strength is comparatively low in relation to the value of the assets being protected.
In a recent research endeavor, researchers examined instances of weak password-based
authentication in healthcare devices, with particular emphasis on external and internal
defibrillators. Consequently, individuals possessing privileges have the capability to modify or
remove the password file and install additional software onto the device. Additionally,
researchers conducted reverse engineering of the healthcare authentication system, developing a
compact utility to either alter or retrieve a user's password. Hence, the suggested system
integrates ZKP alongside a public key cryptosystem to establish robust authentication among
entities, thus addressing such potential attacks. Additionally, employing attribute-based access
control could afford greater granularity in regulating the usage of patient data.
Privilege escalation attacks: A privilege escalation attack exploits vulnerabilities in the operating
system or application, including bugs, design flaws, or configuration errors, to gain unauthorized
access to healthcare devices and data normally restricted by permission or authorization
protocols. These attacks may be instigated by malicious users, such as patients or physicians, who
have legitimate access to healthcare systems and engage in activities like calibration failures or
data tampering. By adopting attribute-based access control, the proposed system copes from these
attacks simply.

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5.2. Performance Analysis
Based on phases and participants, Table 4 depicts comparison of computation overheads between
Liu et al.’s scheme and the proposed one because the other three related works are unclearly
defined on their operations and also were not focused on the healthcare application nor not using
attribute-based access control. For the sake of simplicity and without sacrificing generality, our
focus was directed towards computationally intensive operations such as hash operation (H),
bilinear map (E), pairing operation (P) and blockchain consensus BCc. But the other operations
were ignored, which are cost-lightened operations.
Table 4. Computation overhead comparison
Phases Liu et al. in [15] Proposed
Initialization
RS registration
DU registration
Authentication
Access control
EHR generation
Collaboration
AS: (N+1)E
RS:1E+1H+[1E+1H]
AS:[2E+1H]+1E
DU:1E+1H+[1E+1H]|2E
RS:[2E+1H]+1E+BCc
AS: 3E
DU:[3E+2H]
BC:[4E+2H]+BCc
DU:5E
DUP:4P
-
-
AS: (N+1)E
HS:1E+1H+[1E+1H]
AS:[2E+1H]+1E
PT:1E+1H+[1E+1H]|2E
HS:[2E+1H]+1E+BCc
AS: 3E
PT:[1E+1H]
BC:[2E+1H]+BCc
PT:5E
MD:4P
PT:1E
BC:[4E+2H]+BCc
CD:[1E+1H]
BC:[2E+1H]+BCc
In comparison to Liu et al.’s scheme, the computational burden during the authentication phase of
the proposed system is reduced, as we have streamlined the computational overhead of Zero-
Knowledge Proofs (ZKP) more than Liu et al.’s scheme. However, we provide EHR generation
and retrieval and cross-domain collaboration.
On the other hand, focusing on the communication overhead. The proposed system requires less
size of messages due to now using the proof of equality used in Liu et al.’s scheme. Thereby, the
proposed system has better performance than Liu et al.’s scheme. Additionally, the proposed
system offers greater functionality compared to other related works, as indicated in Table 3.
Table 5. Storage overhead comparison
Data type Liu et al. in [15] Proposed
Attribute key
System key
UPK
USK
AS: (N+1)|Zp
*
|
RS:1|Zp
*
|
DU:1|Zp
*
|+4|G|+|UAtS|
BC:(N+Nu+Ns)|G|
AS: (N+1)|Zp
*
|
RS:1|Zp
*
|
DU:1|Zp
*
|+4|G|+|UAtS|
BC:(N+Nu+Ns)|G|+|Hosp|
+|EHR|+|ACL|+|LOG|
Nu-the number of users, Ns-the number of RSs, Na-the number of attributes in a set
|UAtS|-the cost for storing attribute set, BC-blockchain, DU-data units, |Hosp|-the cost for hospital
information
|HER|-the cost for storing EHR, |ACL|-the cost for storing ACL, |LOG|-the cost for storing logs

134
The storage overhead of the proposed system is detailed in Table 5. In Liu et al.’s scheme and the
proposed system, the storage overhead of AS is minimal (N+1)|Zp
*
|, while that of BC depends on
(N + Nu + Ns)|G|. Similarly, each RS's storage overhead is limited to one its secret key in Zp
*
.
Moreover, the storage overhead of DU remains constant regardless of the number of attributes,
making it suitable for resource-constrained devices. The storage overhead of the proposed system
is similar to Liu et al.’s scheme except additional blockchain overhead, which is
|Hosp|+|EHR|+|ACL|+|LOG|. The overhead is related to the healthcare application, which is to
provide data sovereignty.
Table 6. Communication overhead comparison
Phases Liu et al. in [15] Proposed
Initialization
RS registration
DU registration
Authentication
EHR generation and retrieval
Cross domain collaboration
1D+2|H|+(N+1)|G|+|KDF|
1|ΨPoK|+1|G|+|Txt|
1|ΨPoK|+1|G|+|Txt|
1|ΨPoK|+4|G|+|Txt|
-
-
1D+2|H|+(N+1)|G|+|KDF|
1|ΨPoK|+1|G|+|Txt|
1|ΨPoK|+1|G|+|Txt|
1|ΨPoK|+1|G|+|Txt|
1|Satt|
1|ΨPoK|+1|G|+|Txt|
D-a set of (G, GT, g, p, e()), |H|-one hash function, |Txt|-communication overhead for a blockchain
transaction
Liu et al.’s scheme uses two types of ZKP messages, proof of knowledge (ΨPoK) and proof of
equality (ΨPoE). ΨPoK requires one element of G and one element of Zp
*
but ΨPoE needs three more
elements of G than ΨPoK. Contrast to that the proposed system only requires to use ΨPoK for both
of registration and authentication. The proposed system requires to use EHR generation and
retrieval and cross domain collaboration for healthcare application, which is the core parts of the
proposed system. EHR generation and retrieval requires one attribute-based encryption or
decryption operation Satt. Cross domain collaboration requires the same communication overhead
as the registration or authentication. Thereby, the communication overhead of the proposed
system is lighter than Liu et al.’s scheme as shown in Table 6.
6. CONCLUSIONS AND FUTURE WORK
This paper has proposed a blockchain-enforced attribute-based access control with ZKP for
healthcare service. The previous medical systems have a problem that they keep scattered data
between hospitals, which is difficult to the patients to keep their data sovereignty. The proposed
system employed attribute-based access control, ZKP and blockchain for the healthcare services
security provision. Blockchain is used to keep hospital information in public chain but EHR
related data with ACL in private chain. Furthermore, EHR provides access control by using the
attributed based cryptosystem before they are stored in the blockchain. The envisaged
applicability of the proposed system extends to diverse medical systems utilizing private data. In
the future, we plan to complete the implementation of the system and add some more security and
privacy mechanisms for the further requirements from the medical system.
Personal data treatment is the main security and privacy concern of healthcare applications. Any
utilization or handling of personal data must adhere to the regulations outlined in the General
Data Protection Regulation (GDPR). Adopted on April 14, 2016, the GDPR officially took effect
on May 25, 2018, marking a significant milestone in data protection regulations. Patient data, for
the most part, is considered special personal data, and the GDPR prohibits the processing of
health information unless specific exceptions specified in Article 9 are met. The data must adhere
to the standards outlined by the GDPR to ensure regulatory compliance. The proposed system

135
aims to attain data sovereignty and privacy but adjustments are required to ensure alignment with
GDPR regulations. Moreover, reshaping the proposed system is essential for enhancing both its
feasibility and efficiency by using detailed network evaluation based on the Hypherledger Caliper.
Ultimately, the development of a system that can guarantee patient data sovereignty and provide
security and privacy necessitates integration with real hospital environments.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
The corresponding author is Hyunsung Kim. Seil Kim collaborated with the authors to put his
efforts to collect various resources for the healthcare applications. This research was funded by
R&E program funded by Kyungil University and Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-
2017R1D1A1B04032598).
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Information Theory, 29(2), 198-208.
AUTHORS
Dongju Lee is B.E. degree student in Computer Science at Kyungil University, Korea.
He has been a member of Information Security Laboratory in Kyungil University from
2023. He has been a research member of “Research on Data Centric Security and Privacy
Model for Intelligent Internet of Things” project funded by National Research
Foundation of Korea. His research interests are in Cryptography, Information Security,
Cryptographic Protocol, Privacy, Internet of Things, Blockchain and Cryptanalysis.

137
Hyunsung Kim received the M.Sc. and Ph.D. degrees in computer engineering from
Kyungpook National University, Korea, in 1998 and 2002, respectively. He is a
Professor at the School of Computer Science, Kyungil University, Korea from 2012.
Furthermore, he is currently a visiting professor at the Department of Mathematical
Sciences, Chancellor College, University of Malawi, Malawi from 2015. He also was a
visiting researcher at Dublin City University in 2009. From 2000 to 2002, he worked as a
senior researcher at Ditto Technology. He had been an associate professor from 2002 to 2012 with the
Department of Computer Engineering, Kyungil University. His research interests include cryptography,
VLSI, authentication technologies, network security, ubiquitous computing security, blockchain, and
security protocol.

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