Blockchain for Privacy-Preserving Data Distribution in Healthcare

Amitesh Singh Rajput and Arnav Agrawal

Department of Computer Science & Information Systems, Birla Institute of Technology & Science, Pilani, Vidya Vihar,

Pilani, Rajasthan, India

Keywords:

Blockchain Technology, Healthcare Data Security, Decentralized Data Management,

Smart Contracts in Healthcare, Encryption and Key Management, Authentication Protocols.

Abstract:

As virtual transformation maintains to reshape healthcare, the security and privacy of health information

have become paramount worries. This paper delves into the novel application of blockchain generation as a

strategic technique to these urgent issues. In contrast to traditional centralized information control structures,

blockchain introduces an intensive alternate with its decentralized, immutable, and transparent nature. This

shift gives a robust alternative to comply with sensitive health data. We propose a contemporary, blockchain-

primarily based method to seamlessly integrate existing healthcare records into ledgers and share them in a

controlled way. The proposed method emphasizes enhanced data integrity, advanced security features, and a

patient-centric technique to data governance using customized smart contracts. Experimental results underline

the proposed method’s advanced performance for scalability, protection, and general machine performance,

making a compelling case for its adoption in healthcare records control.

1 INTRODUCTION

In today’s complex digital world, the healthcare sector

is particularly sensitive due to its critical nature. With

an unparalleled emphasis on the sanctity, safety, and

accessibility of facts, healthcare gives multifaceted

challenges. The very lifeblood of medical practices

and studies, patient records, is often saved in cen-

tralized databases. While these centralized structures

have long been taken into consideration as reliable

workhorses, they are being more identiﬁed for their

inherent ﬂaws and vulnerabilities (Aslan et al., 2023).

Centralized healthcare databases, although efﬁ-

cient in many respects, are susceptible to a variety

of challenges. These range from system failures,

cyber-attacks, unauthorized data breaches to poten-

tial data tampering (Newaz et al., 2021). Further-

more, the modern demand for seamless data ﬂow

and integrated healthcare operations often stumbles

against the brick walls of these fragmented and iso-

lated systems. Our study addresses this problem by

providing a blockchain-based model for healthcare

data distribution. Blockchain technology offers a

promising answer to overcome the limitations of con-

ventional centralized systems (Haleem et al., 2021).

In the healthcare sector, where data sensitivity and

privacy are paramount, blockchain can revolutionize

how person’s data is stored, accessed, and shared.

A blockchain-based system can mitigate risks associ-

ated with centralized databases by means of dispens-

ing the data across a network, making it less vulnera-

ble to single points of failure and cyber assaults. This

guarantees better data integrity and protection. Addi-

tionally, blockchain’s transparent nature allows better

trust and auditability in data transactions, that is crit-

ical in healthcare wherein data accuracy and history

are important.

In recent years, several blockchain-based medical

recordkeeping systems have been developed, offer-

ing innovative solutions to the challenges in health-

care data management. For instance, MedRec (Azaria

et al., 2016a) is a notable system that uses Ethereum

blockchain for managing access to medical records.

It provides a decentralized record management sys-

tem to handle authentication, conﬁdentiality, account-

ability, and data sharing. HealthChain (Chenthara

et al., 2020) focuses on ensuring data integrity and

access control in healthcare records. It allows for

a secure and transparent record-keeping mechanism

that patients and providers can trust. MediBlock

(Vishwa, 2021) is another signiﬁcant advancement in

this space, providing a patient-centered approach. It

emphasizes patient consent and uses blockchain to se-

cure medical records.

Key Authorities (KAs) are essential in

blockchain-based healthcare data systems. They

Rajput, A. and Agrawal, A.

Blockchain for Privacy-Preserving Data Distribution in Healthcare.

DOI: 10.5220/0012470500003648

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information Systems Security and Privacy (ICISSP 2024), pages 621-631

ISBN: 978-989-758-683-5; ISSN: 2184-4356

621

are accountable for dealing with cryptographic keys,

making sure that the data is secure, maintaining

data integrity, facilitating secure transactions, and

contributing to system scalability. KAs play an

important role in protecting sensitive healthcare data,

making them essential to the security and capability

of such systems. Despite the innovative and potential

solutions for the health records, previous researches

(Azaria et al., 2016a)(Chenthara et al., 2020)(Vishwa,

2021) have not emphasized on this fundamental part.

We consider this part as utmost important along with

robust patient data sharing and propose a blockchain

based automated approach in this paper. We propose

a comprehensive set of rules, intricately designed,

showcasing how blockchain can be seamlessly

integrated into the healthcare facts pipeline involving

an extensive functioning for KAs. By examining its

core attributes, we unravel how blockchain could be

the keystone in constructing a robust healthcare data

ecosystem.

This paper aspires to make a case for a blockchain-

driven healthcare data revolution. In the follow-

ing sections, we will discover a meticulous break-

down of current challenges in section 2, our proposed

blockchain solution and its practical algorithmic de-

scriptions in section 3, followed by a thorough ex-

perimental analysis and comparison with the existing

blockchain methods in section 4. We believe that our

research not only contributes extensively to academic

discourse but additionally paves the manner for prac-

tical, tangible upgrades in the real world of healthcare

data management.

2 LITERATURE REVIEW

Data distribution, especially in an age of escalat-

ing cybersecurity threats and stringent privacy regu-

lations, is a domain of paramount importance. Ad-

dressing countless challenges in this area necessitates

a holistic understanding of both the historical context

and the forefront of current advancements. This lit-

erature review describes the landscape of data distri-

bution, particularly emphasizing the following three

pivotal facets:

1. Challenges of Traditional Data Distribution

Systems. An exploration into the pitfalls and

vulnerabilities of legacy systems, highlighting the

need for innovative solutions.

2. Blockchain’s Emergence in Privacy-Preserving

Data Distribution. Recognising blockchain’s

revolutionary potential to reimagine safe and

transparent data distribution paradigms.

3. Existing Access Control Methods Integrating

Multiple Entities in the Overall System. A

thorough examination of prior methods combin-

ing several entities to overcome current obstacles.

2.1 Challenges of Traditional Data

Distribution Systems

Traditional data distribution systems play an integral

role in various sectors, especially in the age of dig-

ital transformation. However, their inherent design

and operational characteristics present multiple chal-

lenges.

Centralized data distribution boasts ease of man-

agement, uniformity of data versions, and straightfor-

ward administrative control (Inclusion Cloud, 2023).

An attack on this system can render the entire data dis-

tribution network vulnerable resulting in irreversible

data loss (Mohammed et al., 2010).The linear scal-

ing model of infrastructure leads to longer response

times. In cases even system downtime. This nega-

tively impacts both user experience and operational

efﬁciency (Bertin et al., 2009). Centralized data

servers are typically located in different regions. Con-

sequently they become subject to laws that may not

align with operations or could impose restrictions

on data access. This can lead to inconsistencies in

operations (Salmon and Myers, 2019).With all data

stored in a location there is a risk of monopolization

where the controlling entity may misuse the informa-

tion (McIntosh, 2018). Without immutable record-

keeping, changes to data can go undetected. This

lack of an unalterable history can result in large com-

mercial enterprise disputes, mainly in sectors wherein

data sanctity is paramount (Pandey et al., 2020). Tra-

ditional systems might not always provide mecha-

nisms for real-time data validation, making it difﬁcult

to immediately detect and rectify data inconsistencies

(Kumar and Bhatia, 2020).

The centralized model, in many instances, does

not maintain transparent data modiﬁcation logs. Such

opacity makes it challenging to trace data changes,

leading to potential trust breaches (Abiteboul and

Stoyanovich, 2019). All decisions, including data ac-

cess, distribution rules, and dispute resolutions, are at

the discretion of a central authority. Such a system

might not always be in the best interests of all stake-

holders involved (Laoutaris, 2018). The centralized

nature necessitates regular maintenance, leading to re-

current costs and potential downtimes (Chukmaitov

et al., 2015). They consume enormous amounts of en-

ergy, leading to operational inefﬁciencies and larger

carbon footprints (Chukmaitov et al., 2015). Central-

ized servers, especially during peak times, can face

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

622

bandwidth constraints, slowing down data dissemina-

tion rates (Hugoson, 2009).

Researchers and developers around the world have

been investigating alternate paradigms for data deliv-

ery in light of these difﬁculties. With its decentralised

structure, immutable record-keeping, and strong se-

curity features, blockchain technology has gained at-

tention as a possible solution to several of these issues

(Bhutta et al., 2021).

2.2 Blockchain’s Emergence in

Privacy-Preserving Data

Distribution

Blockchain technology stands out as a highly promis-

ing option for data distribution including built-in at-

tributes like traceability, transparency, and resistance

to tampering (Attaran, 2022). Numerous approaches

that take advantage of blockchain technology have

been put out and investigated; these have primarily

concerned healthcare and distribution while maintain-

ing anonymity. Here, we examine some of the most

signiﬁcant developments in this ﬁeld, shedding light

on novel approaches and their consequences.

2.2.1 Blockchain in Healthcare: A Paradigm

Shift

Blockchain, at the start conceptualized for the Bitcoin

cryptocurrency (Nakamoto, 2008), has found great

applications in healthcare, especially in protecting the

integrity, authenticity, and conﬁdentiality of health

records (Azaria et al., 2016b). The decentralized and

immutable nature of the blockchain ensures that every

transaction, or in the case of healthcare, every data en-

try or access, is transparently recorded and veriﬁed by

a network of peers.

MedRec, introduced by Azaria et al. (Azaria

et al., 2016b), is an inﬂuential work that offers a

decentralized electronic health records system using

Ethereum’s blockchain. This system presents solu-

tions to the challenges of data interoperability, ac-

cess controls, and auditability in traditional EHR sys-

tems. By granting patients the power to grant ac-

cess to their health records and ensuring a transpar-

ent record of data access, MedRec paves the way for

improved patient-centric healthcare.

Furthermore, Yue et al. (Yue et al., 2016) pro-

posed a scheme named “Healthcare Data Gateways”,

leveraging blockchain to ensure data security, privacy,

and interoperability in health information exchange.

The framework emphasizes the role of patient con-

sent in data sharing and makes use of smart contracts

to automate and enforce data access policies.

2.2.2 Privacy-Preserving Distribution with

Blockchain

While healthcare is a predominant domain, the ex-

tensive theme of privacy preservation facilitated by

blockchain extends to various data distribution mod-

els. Zhang et al. (Zhang and Wen, 2017) intro-

duced a privacy-preserving framework which utilizes

blockchain for secure and transparent ﬁle access in

cloud environments. This framework harnesses the

decentralized consensus mechanism of blockchain to

deter unauthorized data access and modiﬁcations.

Another notable advancement is the work of Liang

et al. (Liang et al., 2017), where they propose a credit-

based scheme for privacy-preserving data sharing in

the context of the Internet of Things (IoT). Their

methodology combines the blockchain with a credit

system, ensuring that data providers and consumers

maintain a trust-based relationship, with transactions

transparently recorded and veriﬁed on the blockchain.

In addressing the challenge of non-interoperable

medical data sharing, there is a blockchain-based sys-

tem employing symmetric encryption, access con-

trol, and participant power restriction. Their sys-

tem ensures privacy by encrypting medical data, uti-

lizing a novel blockchain framework, implementing

chameleon signatures, and enabling revocable partic-

ipant privileges. Security analysis validates its ro-

bustness, and experiments demonstrate superior time

overhead compared to alternatives (Hu et al., 2023).

In response to security challenges in medical

data transmission and sharing, Chen proposed a

blockchain-based medical information system ensur-

ing data integrity and privacy. Their model employs

IoT for real-time patient health record collection and

introduces a secure, anonymous data sharing scheme

based on cloud servers and proxy re-encryption. Im-

plemented on Hyperledger Fabric, the system features

a dual-channel architecture and medical chaincode for

efﬁcient data management and access control. (Chen

et al., 2021).

Huang presented a blockchain-based privacy-

preserving scheme addressing the challenge of bal-

ancing patient privacy with the demands of health

data research and commerce. The proposed scheme

enables secure sharing of medical data among en-

tities, including patients, research institutions, and

semi-trusted cloud servers. Utilizing zero-knowledge

proof for privacy-preserving veriﬁcation and proxy

re-encryption for intermediary data decryption, the

scheme ensures data availability and consistency.

(Huang et al., 2020).

Blockchain for Privacy-Preserving Data Distribution in Healthcare

623

2.3 Existing Access Control Methods

Integrating Multiple Entities in the

Overall System

The landscape of data distribution has seen countless

techniques aimed at ensuring robustness, efﬁciency,

and security. A few of the previous methods (Hilde-

brandt et al., 2016)(Erkin et al., )(Rajput and Balasub-

ramanian, 2021) heavily rely with an integration of

multiple entities including KAs, transcryptors (TRs),

doctors, storage facilities, and medical researchers.

The TRs serves as intermediary entities. When a

request for data access is made, it is the TR’s role

to fetch the corresponding cryptographic key from

the KA, perform the necessary decryption operations,

and furnish the requester with the requisite decrypted

data. Other entities may encompass data providers,

end-users, or even audit entities ensuring the system’s

integrity and adherence to established protocols. The

functioning is performed with the data provider en-

crypting the data using cryptographic keys sourced

from the KAs. Once encrypted, the data is set for

distribution. Upon receipt of a valid data access re-

quest, the TR gets into action. It fetches the associated

cryptographic key, decrypts the data, and ensures its

timely delivery to the requester.

One strong challenge with these methods is their

reliance on centralized entities, i.e., the KAs. Central-

ization, while offering streamlined operations can be

a vulnerability concern. Compromising the KA could

potentially jeopardize the entire system’s data secu-

rity. Latency is another big concern which needs to be

taken care of. Given the multiple interactions between

entities, especially between TR and KAs for key re-

trieval, time-sensitive operations can be adversely af-

fected. While the algorithm integrating KAs, TRs,

and other entities showcased potential in addressing

data distribution’s challenges, it wasn’t devoid of lim-

itations. The centralization concerns, latency issues,

and scalability bottlenecks underlined the need for al-

ternative mechanisms that could offer both efﬁciency

and robust security. It is within this context that the

exploration for newer methods, including those har-

nessing blockchain, emerged.

3 PROPOSED METHOD

This section describes the proposed blockchain-based

method, designed to triumph over the restrictions of

traditional data distribution systems, particularly in-

side the healthcare sector. At its center, our ap-

proach leverages the decentralized, transparent, and

immutable nature of blockchain generation. The ap-

proach is multifaceted, integrating advanced crypto-

graphic strategies, smart contracts, and consensus al-

gorithms to enhance data integrity, security, and trans-

parency. We aim to decentralize data management for

getting rid of single points of failure, improving scala-

bility, and increasing resistance to cyber threats. Fur-

thermore, the proposed approach introduces a patient-

centric version for data governance, ensuring privacy

and hold over personal health records.

In the subsequent subsections, the critical compo-

nents of the proposed approach are described. Also,

the setup of blockchain-based identity management,

secure packet sharing mechanisms, and the processes

for master key generation, data request handling, and

user veriﬁcation are covered. These additives col-

lectively form a cohesive and robust framework for

healthcare data management, ready to be incorporated

into current healthcare IT infrastructures. The de-

scription contains various specialized algorithms im-

portant with respect to the proposed solution. It is im-

portant to note that the functions in these algorithms

are named to clearly indicate their purpose. This nam-

ing approach helps in easily understanding what each

function does and how it ﬁts into the overall system.

In light of the limitations encountered with the tra-

ditional data distribution techniques and the inefﬁca-

cies linked to the previously discussed algorithm in-

tegrating KAs, TRs, and other entities, the proposed

proposition pivots toward leveraging the capabilities

of blockchain technology. The emphasis here is not

only to decentralize the data distribution process but

also to harness the inherent security and transparency

features of blockchain. The proposed method lever-

ages customized smart contracts that play a pivotal

role to accomplish the desired tasks. Essentially,

smart contracts are self-executing contracts where the

agreement between the sender and the receiver is di-

rectly written into lines of code. In the context of our

data distribution system, the smart contracts will au-

tomate the validation of data access requests and the

distribution of decryption keys.

In the proposed method, smart contracts exe-

cute algorithms linearly for optimal functionality.

The sequence begins with Identity Registration and

Blockchain Logging (Algorithm 1), establishing veri-

ﬁed user identities on the blockchain. Next, the Ran-

dom Selection of Key Authorities (Algorithm 2) en-

sures unbiased selection of Key Authorities. This

is followed by Secure Veriﬁcation Packet Sharing

(Algorithm 3), distributing veriﬁcation data to KAs.

Subsequently, Master Key Generation and Validation

(Algorithm 4) securely generates encryption keys,

and Distribution of Veriﬁcation Packets (Algorithm

5) ensures secure data dissemination to KAs. Data

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624

Request, Veriﬁcation & Blockchain Logging (Algo-

rithm 6) then manages and authenticates data re-

quests. Master Key Derivation & Decryption Process

(Algorithm 7) and Data Decryption & Sharing (Al-

gorithm 8) handle the secure decryption and distribu-

tion of data. The process concludes with Repetitive

User Veriﬁcation (Algorithm 9), maintaining contin-

uous security checks. This ordered execution of al-

gorithms as smart contracts solidiﬁes the system’s in-

tegrity and efﬁciency in healthcare data management.

3.1 Setting up an Identity and

Registering on the Blockchain

In this section, the algorithm describes the way of

organising a person’s identiﬁcation inside the sys-

tem and securing it via blockchain technology. The

method begins with the generation of a completely

unique identiﬁer, accompanied by the introduction of

a digital credential which is then stored in a database

for quick access. To ensure the individuality and pre-

vent duplication of identities, the credential is veriﬁed

with the prevailing facts on the blockchain. If the cre-

dential is new, it is registered at the blockchain; oth-

erwise, a duplication error is reported. This approach

as present in the algorithm 1 ensures that every person

within the machine has a distinct and veriﬁable digital

identiﬁcation.

Algorithm 1: Identity Registration and Blockchain

Logging.

1: Input: User’s name name, blockchain BC

2: Output: Registration status

3: uniqueID ← GENERATEUNIQUEID(name)

4: credential ← (name, uniqueID)

5: STORECREDENTIALS(credential)

6: if ¬VERIFYIDENTITY(credential, BC) then

7: REGISTERONBLOCKCHAIN(credential, BC)

8: else

9: print ”Error: Duplicate identity.”

10: end if

11: function VERIFYIDENTITY(credential, BC)

12: for all block ∈ BC do

13: if block.data = credential then

14: return true

15: end if

16: end for

17: return false

18: end function

19: function STORECREDENTIALS(credential)

20: // Store credentials in the database

21: end function

22: function REGISTERONBLOCKCHAIN(credential, BC)

23: // Add new identity to the blockchain

24: end function

3.2 Secure Veriﬁcation Packet Sharing

This section establishes a procedure for the secure

creation and dissemination of veriﬁcation packets,

crucial for the operations of Key Authorities within

the blockchain network. It details a series of steps,

mentioned in algorithm 2, that begin with the genera-

tion of a veriﬁcation packet, which is uniquely iden-

tiﬁed by a hash and timestamp to ensure its integrity.

These packets are then stored securely until they are

to be shared with eligible Key Authorities, with each

sharing event logged on the blockchain to enforce

transparency and traceability.

3.3 Random Selection by TR and

Recording on Blockchain

The selection steps in algorithm 3 operates with a

dual aim: To guarantee fairness in the selection pro-

cess of KAs and to log each selection event onto

the blockchain, thus embedding transparency and im-

mutability into the system. It begins by identifying

KAs that have not been recently active, thereby ensur-

ing an unbiased opportunity for all. A rigorous safety

check follows, verifying the temporal gap since each

KA’s last selection, thus preventing repetitive selec-

tions. Successful candidates are then recorded on the

blockchain, time-stamped to uphold the integrity and

veriﬁability of the process.

Algorithm 2: Random Selection of KA with Block

Addition.

1: Input: Pool of Key Authorities KA Pool, blockchain BC

2: Output: Selected Key Authority selectedKA

3: function SELECTKEYAUTHORITY(KA Pool)

4: selectedKA ← Select KA not recently chosen

5: if SAFETYCHECK(selectedKA) then

6: RECORDSELECTION(selectedKA, BC)

7: else

8: print ”Selection failed: KA chosen recently.”

9: end if

10: end function

11: function SAFETYCHECK(selectedKA)

12: timeDi f f ← Time since selectedKA’s last selection

13: return timeDi f f ≥ 24 hours

14: end function

15: function RECORDSELECTION(selectedKA, BC)

16: ADDBLOCK(BC, ”SELECTION”, selectedKA, ”current time”)

17: end function

Blockchain for Privacy-Preserving Data Distribution in Healthcare

625

Algorithm 3: Secure Veriﬁcation Packet Sharing.

1: Input: Data for Veriﬁcation Packet data, Key Author-

ities KA

2: Output: Status of Veriﬁcation Packet Sharing

3: function CREATEANDSHAREVERPACKET(data, KA)

4: packet ← CREATEPACKET(data)

5: for all ka ∈ KA do

6: if ISELIGIBLE(ka) then

7: SHAREPACKET(packet, ka)

8: LOGPACKETSHARING(packet, ka)

9: end if

10: end for

11: end function

3.4 Master Key Generation with

Blockchain Validation

The algorithm 4 outlines a secure process for Master

Key (MK) generation, encryption, and storage, cou-

pled with blockchain logging for validation. The MK,

generated with a secure random function and times-

tamped, is encrypted for conﬁdentiality. It’s then

stored in a secure database, with blockchain logging

ensuring transparency and validation checks main-

taining generation at safe intervals.

3.5 Distribution of Veriﬁcation Packets

The secure and efﬁcient distribution of Veriﬁca-

tion Packets (VP) to Key Authorities (KA) within a

blockchain network is the basis of this system. The

algorithm initiates with the generation of a unique

packet ID and its subsequent encryption. KAs are

selected based on speciﬁc criteria for packet distri-

bution, with the entire process being logged onto the

blockchain for accountability. Duplicate distributions

are prevented through a veriﬁcation mechanism, en-

suring the integrity of the system. All this can be ver-

iﬁed from the Algorithm 5.

Algorithm 4: Master Key Generation and Validation.

1: Input: Security parameters, blockchain BC

2: Output: Master Key Generation and Validation

3: function GENERATEANDVALIDATEMASTERKEY

4: masterKey ← GENERATEMASTERKEY

5: encrKey ← ENCRMASTERKEY(masterKey)

6: STOREENCRYPTEDKEY(encrKey)

7: if VALIDATEKEYGENERATION then

8: LOGKEYGENERATION(encrKey, BC)

9: else

10: print ”Key generation validation failed.”

11: end if

12: end function

3.6 Data Request, Veriﬁcation &

Blockchain Logging

This subsection outlines the streamlined process of re-

ceiving data requests, verifying requester authenticity,

and logging the transactions on the blockchain. The

Algorithm 6 handles requests with a security-focused

approach, ensuring data integrity and access control.

3.7 Master Key Derivation &

Decryption Process

The Algorithm 7 below starts with the derivation of a

master key (MK) which is an important step in en-

suring the security of the system. It is created us-

ing a secure function and is timestamped to guaran-

tee uniqueness. After derivation, the MK is encrypted

and stored securely. It is then utilized for decrypting

data, enforcing conﬁdentiality. The system logs each

step involving the MK on the blockchain.

Algorithm 5: Distribution of Veriﬁcation Packets.

1: Input: Veriﬁcation Packet data data, Key Authorities

2: Output: Distribution status

3: function DISTRIBUTEVERPACKET(data, KA)

4: packetID ← GENERATEPACKETID(data)

5: encryptedPacket ← ENCRYPTPACKET(data)

6: selectedKAs ← SELECTKASFORDISTRIBUTION

7: for ka in selectedKAs do

8: LOGDISTRIBUTIONEVENT(packetID, ka)

9: DISTRIBUTEPACKET(encryptedPacket, ka)

10: print ”Packet distributed to KA”

11: end for

12: CONFIRMFROMKA(packetID, selectedKAs)

13: end function

Algorithm 6: Data Request, Veriﬁcation & Blockchain

Logging.

1: Input: Request from entity requester, data identiﬁer

dataID

2: Output: Acknowledgment of successful data handling

3: function HANDLEDATARE-

QUEST(requester, dataID, BC)

4: isAuthentic ← VERIFYREQUESTER(requester)

5: if isAuthentic & SAFETYCHECK(requester) then

6: data ← FETCHDATA(dataID)

7: encryptedData ← ENCRYPTDATA(data)

8: TRANSMITDATA(encryptedData, requester)

9: LOGTRANSACTION(requester, dataID, BC)

10: return AWAITRECEIPT(requester)

11: else

12: print ”Request denied.”

13: return False

14: end if

15: end function

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3.8 Data Decryption & Sharing

The secure distribution of Veriﬁcation Packets is a

critical characteristic in our blockchain-based system,

in particular for Key Authorities (KA) who rely upon

these packets for operational data. Each packet is

created with a completely unique identiﬁer, encapsu-

lated securely, after which it is transmitted to a se-

lected group of KAs. This method as mentioned in the

Algorithm 8 guarantees information integrity through

encryption and maintains transparency by recording

each distribution motion at the blockchain.

Algorithm 7: Master Key Derivation and Decryption.

1: Input: inputSeed, encryptedData

2: Output: decryptedData

3: function DERIVEANDENCRYPTMK(inputSeed)

4: masterKey ← derive key using inputSeed

5: encryptedMK ← encrypt masterKey

6: Store encryptedMK securely

7: Log MK creation on blockchain

8: return encryptedMK

9: end function

10: function DECRYPTWITHMK(encryptedData, masterK)

11: decryptedData ← decrypt encryptedData with

masterKey

12: Log decryption on blockchain

13: return decryptedData

14: end function

Algorithm 8: Distribution of Veriﬁcation Packets.

1: Input: Veriﬁcation Packet data data, Key Authorities

KAs

2: Output: Distribution status

3: function DISTRIBUTEVERIFICATION-

PACKET(data, KAs)

4: packetID ← GENERATEPACKETID(data)

5: encryptedPacket ← ENCRYPTPACKET(data)

6: selectedKAs ← SELECTKASFORDISTRIBUTION

7: for all ka ∈ selectedKAs do

8: if SAFETYCHECK(ka) then

9: SENDPACKET(encryptedPacket, ka)

10: LOGPACKETSHARING(packetID, ka)

11: print ”Packet distributed to KA: ka”

12: else

13: print ”Distribution to KA: ka failed due to

safety check.”

14: end if

15: end for

16: return CONFIRMRECEIPT(selectedKAs)

17: end function

3.9 Repetitive User Veriﬁcation

The Repetitive User Veriﬁcation process mentioned

in the Algorithm 9 within the system is designed to

set up and preserve the authenticity of user identities.

By capturing user details and validating identities, the

device ensures that only legitimate users can get entry

to sensitive information. The blockchain logs each

veriﬁcation attempt, providing an immutable record

of user activity. Enhanced safety features, along with

multi-factor authentication and checks for fraudulent

tries, shield against unauthorized access. Any unusual

activities are monitored and reported, underscoring

the system’s commitment to security and user trust.

Algorithm 9: Repetitive User Veriﬁcation.

1: Input: User ID userID, Veriﬁcation Details

userDetails

2: Output: Veriﬁcation Status

3: function VERIFYUSER(userID, userDetails)

4: details ← CAPTUREUSERDETAILS(userDetails)

5: if VALIDATEUSERIDENTITY(details) then

6: LOGVERIFICATIONEVENT(userID, success)

7: INITIATEMULTIFACTORAUTHENTICA-

TION(userID)

8: print ”User veriﬁed successfully.”

9: else

10: if SAFETYCHECKFORFRAUDULENTAT-

TEMPTS(userID) then

11: LIMITUNSUCCESSFULAT-

TEMPTS(userID)

12: REPORTSUSPICIOUSACTIVITY(userID)

13: print ”User veriﬁcation failed.”

14: end if

15: end if

16: end function

4 EXPERIMENTAL RESULTS

The experimental results presented here are derived

from simulations that model the overall performance

of the proposed blockchain-based system. Our com-

prehensive evaluation specializes in several key per-

formance indicators inclusive of throughput, latency,

scalability, security, and smart contract execution

metrics. The simulations have been performed on a

virtualized test network replicating a medium-scale

enterprise environment to offer a realistic evaluation

of system capabilities.

4.1 Simulation Environment

The testing environment set up speciﬁcations include

Ubuntu 20.04 LTS Operating System, Intel Xeon

CPU E5-2698 v4 @2.20GHz Processor, 64GB RAM,

Blockchain for Privacy-Preserving Data Distribution in Healthcare

627

1 TB SSD, Ethereum Testnet blockchain platform,

followed by Proof of Authority (PoA) consensus al-

gorithm, and 25 validator nodes.

The selection of Proof of Authority (PoA) as the

consensus mechanism is rooted in its suitability for

healthcare data management. PoA provides a secure

and efﬁcient environment, crucial for handling sen-

sitive health records. Key reasons for choosing PoA

include:

• Trust and Identity Veriﬁcation. PoA ensures

validators are known entities, enhancing trust and

security, essential in healthcare for regulatory

compliance.

• Efﬁcient Transaction Processing. PoA offers

faster transaction times compared to PoW or PoS,

crucial for real-time healthcare applications.

• Energy Efﬁciency. Less energy-intensive than

PoW, PoA aligns with sustainable IT practices in

healthcare.

• Controlled Network Access. Restricts network

participation to veriﬁed entities, maintaining data

privacy and adhering to healthcare regulations.

• Balance of Centralization. PoA’s selective val-

idator approach mitigates risks associated with

centralization, ensuring decentralized control.

4.2 Throughput and Latency

The table showcasing throughput and latency illus-

trates how the system effectively handles growing

numbers of transactions. As the transaction count

rises from 100 to 5000, the system’s throughput, mea-

sured in Transactions Per Second (TPS), increases.

However, past a certain point (at 5000 transactions), a

slight descrease in throughput is found, probably be-

cause of the increased load. Latency, or the time taken

to complete a transaction, understandably increases

with the range of transactions, but stays inside a rea-

sonable range, showing the system’s responsiveness

even under heavy load.

Table 1: System Throughput and Latency.

Number of Trans-

actions

Throughput

(TPS)

Latency

(Seconds)

100 50 2

500 150 4

1000 200 6

5000 180 10

4.3 Scalability Analysis

The Scalability analysis table reﬂects the system’s

ability to maintain overall performance as the nodes

increase in number. The average transaction time

shows a slow growth as extra nodes are brought, in-

dicating a modest effect on performance. This up-

ward push is highly linear and mild, suggesting that

the device scales well. The capacity to deal with extra

nodes with only a minor increase in transaction time

is a strong indicator of the system’s scalability and its

capacity for larger-scale deployment.

Table 2: Scalability Test Results.

Number of Nodes Average Transaction

Time (Seconds)

10 2

20 2.2

30 2.5

40 2.75

50 3

4.4 Security Analysis

The security evaluation was executed to evaluate the

resilience of the proposed blockchain-based system

against various attack vectors, taking into account its

unique structure and functionalities. The evaluation

covered exclusive threat models which can be rele-

vant to the system’s operational context in healthcare

data management.

4.4.1 Threat Models and Resistance

Mechanisms

1. DDoS Attack. The system’s decentralized nature,

combined with PoA consensus, inherently resists

DDoS attacks, as attackers cannot target a single

central point. The distributed validator nodes en-

sure continuous network functionality even under

high load.

Proof. The blockchain system employs a dis-

tributed network of validator nodes. Let N be the

total number of nodes in the network. The prob-

ability P of successfully disrupting the network

is given by P =





, where n is the number of

nodes an attacker needs to compromise. Given the

large number of nodes in a distributed system, this

probability approaches zero, thus demonstrating

the resilience of the system against DDoS attacks.

2. Double-Spending. The blockchain’s immutable

ledger prevents double-spending by maintain-

ing a transparent and unalterable transaction his-

ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy

628

tory. Each transaction is veriﬁed and recorded

across multiple nodes, making fraudulent at-

tempts highly impractical.

Proof. Assume a transaction T is initiated. For

T to be double-spent, it must be replicated as T

′

However, the blockchain’s consensus mechanism

requires that all transactions be veriﬁed by mul-

tiple nodes. The probability of T

′

being accepted

by all verifying nodes without detection is negligi-

bly small. Hence, the system effectively mitigates

the risk of double-spending.

3. Sybil Attack. The PoA mechanism requires val-

idators to be pre-approved, limiting the potential

for Sybil attacks. Validators are trusted entities,

reducing the risk of malicious nodes entering the

network.

Proof. Consider a network with a set of pre-

approved validators using the PoA consensus. Let

V be the set of all validators and S be the subset of

malicious validators an attacker attempts to intro-

duce. The system’s trust model and validator ap-

proval process ensure that |S| ≪ |V | that reduces

the effectiveness of any Sybil attack.

4. Insider Threats. Smart contracts use for critical

operations such as data access and identity ver-

iﬁcation minimizes human intervention, thereby

reducing the risk of insider threats.

Proof. Smart contracts in the system au-

tonomously execute predeﬁned tasks without hu-

man intervention. Let I be the set of all poten-

tial insider attackers. The absence of direct con-

trol over data by any member of I signiﬁcantly re-

duces the probability of successful insider threats,

ensuring the security of the system against such

attacks.

Each threat model was addressed through a com-

bination of blockchain’s inherent features and speciﬁc

design choices made for the system. While analysing

security breach attempts, different attack types were

rigorously examined against our systems. There were

approximately 1000 attempts of Distributed Denial

of Service (DDoS) attacks, 500 attempts of Double-

Spending attacks, 300 Sybil attacks, and 200 in-

stances of Insider Threats. These type of attempts re-

sulted in a 0% breach success rate, demonstrating the

robustness and effectiveness of our security measures.

4.5 Smart Contract Performance

Table 3 provides insights into the performance of key

smart contracts inside the system. It gives informa-

tion regarding the execution time and gas fee for ev-

ery agreement, such as Identity Registration, Data Re-

quest, and Veriﬁcation Packet. The execution times

are brief, demonstrating the system’s performance in

processing these critical operations. The gas costs,

a measure of the computational resources required,

are notably low, indicating an economically feasible

model for running these contracts on the blockchain

network.

Table 3: Smart Contract Execution Metrics.

Smart Contract Execution

Time (ms)

Gas

Cost

Identity Registration 150 0.0003

Data Request 200 0.0004

Veriﬁcation Packet 250 0.0002

4.6 Discussion and Comparative

Analysis with MedRec

Experimental results validate the technical and prac-

tical viability of the proposed blockchain-based sys-

tem. It successfully handles a huge quantity of trans-

actions with minimal latency, an important feature for

healthcare applications requiring real-time data pro-

cessing. The scalability evaluations show that the sys-

tem sustains performance despite increased network

size, appropriate for tremendous deployment.

The system demonstrates strong resilience to-

wards common cyber threats like DDoS attacks,

double-spending, Sybil attack and Insider threats, un-

derlining its robustness for healthcare data manage-

ment wherein safety and patient privacy are essen-

tial. Smart contracts, pivotal for automating key pro-

cesses along with identiﬁcation veriﬁcation and data

requests, perform successfully in terms of execution

time and useful resource usage. The experimental

outcomes suggest the machine’s readiness for health-

care implementation. Its extremely good throughput,

low latency, protection and scalability, coupled with

smart contract performance, gift it as a promising an-

swer for transforming healthcare data control.

A comparative analysis between the exist-

ing blockchain based healthcare data management

method MedRec (Azaria et al., 2016a) and the pro-

posed method is presented in Table 4. This compre-

hensive assessment aims to highlight the distinctions

and similarities in both the approaches to data protec-

tion, patient consent, interoperability, and other im-

portant aspects of healthcare data management.

The comparative analysis sheds light on the re-

spective strengths and improvement areas of both sys-

tems. While the MedRec system lays a foundational

framework using blockchain for healthcare data, our

proposed approach builds and expands upon these

Blockchain for Privacy-Preserving Data Distribution in Healthcare

629

Table 4: Comparison of proposed method and MedRec

(Azaria et al., 2016a).

Feature Proposed

method

MedRec

(Azaria et al.,

2016a)

Blockchain

Platform

Utilizes a custom

blockchain archi-

tecture.

Employs

Ethereum

blockchain.

Data Security Enhanced

through ad-

vanced crypto-

graphic tech-

niques.

Relies on

Ethereum’s

inherent security

features.

Patient Con-

sent

Automated con-

sent via smart

contracts.

Patient consent

managed through

Ethereum trans-

actions.

Interoperability Designed for

seamless integra-

tion with existing

healthcare sys-

tems.

Focuses on

interoperabil-

ity within

the Ethereum

ecosystem.

Data Access

Control

Controlled by

smart contracts

for precise data

governance.

Access control is

managed through

Ethereum smart

contracts.

Scalability Addresses scala-

bility with a tai-

lored approach to

healthcare data.

Faces typi-

cal scalability

challenges of

Ethereum.

Consensus

Mechanism

Uses Proof of

Authority (PoA)

for efﬁciency and

security.

Dependent on

Ethereum’s

consensus mech-

anism.

Patient Data

Privacy

Prioritizes pa-

tient privacy with

decentralized

data manage-

ment.

Ensures pri-

vacy through

blockchain’s

transparent na-

ture.

Audit Trails Transparent and

immutable audit

trails for data

transactions.

Similar approach

using Ethereum’s

ledger.

Customizability High degree of

customization

for healthcare

needs.

Limited by

Ethereum’s plat-

form constraints.

concepts. By introducing enhanced scalability, cus-

tomizability, and better integration capabilities, the

proposed method demonstrates considerable advance-

ments over the MedRec system, further advancing

blockchain technology’s application in healthcare.

5 CONCLUSION

This research has considerably explored the potential

of blockchain generation in revolutionizing the data

distribution mechanisms in healthcare. Through an

intensive examination of the constraints inherent in

conventional centralized structures, we have analyzed

the transformative impact of blockchain’s decentral-

ized, transparent, and immutable nature. The pro-

posed blockchain-primarily based method not only

addresses the key worrying conditions of data in-

tegrity, security, and transparency but additionally in-

troduces advanced interoperability and patient-centric

data handling. Experimental results conﬁrm the

prevalence of the blockchain approach in terms of

throughput, latency, scalability, and safety, indicating

its readiness for actual-world implementation. This

study reafﬁrms blockchain’s ability in healthcare data

control and also sets a basis for future exploration

into its broader applications in various sectors. Fu-

ture research may additionally focus on the practical

deployment challenges, integration with present sys-

tems, and exploring the synergy among blockchain

and different emerging technology which includes AI

and IoT in healthcare.

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