Blockchain‑Integrated Secure Healthcare Ecosystem: A Scalable and

Privacy‑Compliant Framework for Real‑Time Patient Data

Protection

Venkateswarlu Sunkari

, Fantahun Bogale

, M. Maria Sampoornam

, M. Ratna Sirisha

Issac Jenish J. A.

and Syed Zahidur Rashid

School of Information Technology and Engineering, College of Technology and Built Environment, Addis Ababa

University, Addis Ababa, Ethiopia

Department of Information Technology, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of Computer Science and Engineering (CSE), CVR College of Engineering, Hyderabad -501510, Telangana,

India

Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong,

Bangladesh

Keywords: Blockchain, Healthcare Security, Patient Data Privacy, Smart Contracts, HIPAA Compliance.

Abstract: In the changing ecosystem of e-health, protecting the privacy and security of patient data is not only a

regulatory requirement, but also a technical challenge. Although some literature points in that direction,

several conceptual frameworks developed in the literature have limitations in terms of real application,

scalability, or connection with compliance. In this paper we introduce a concept for an ecosystem for realtime-

enabled secure data sharing in the healthcare domain, where a security researchers and ethical hackers, using

the provenance of a blockchain, enhance the learning in privacy preserving techniques in the context of digital

regulatory compliance. Leveraging lightweight consensus mechanisms and storing data on the blockchain

using smart contracts for fine-grained privacy control, the proposed system enables data integrity, patient-

centric control and adherence to internationally recognized privacy legislations such as HIPAA and GDPR.

Compared with existing proposals that are at the conceptual or highly specific level, the proposed framework

is demonstrated on real clinical data environments, and is validated, scalable and deployable. The findings

show an overall better performance in access control latency, breach-detection accuracy, and audit

transparency of such system, laying the ground for more secure, trusted, and resilient digital healthcare

infrastructures.

1 INTRODUCTION

Healthcare has been digitized and the practice of

generating, sharing, and storing medical information

revolutionized. From EHRs and remote patient

monitoring to telehealth platforms and connected

diagnostic devices, there is more sensitive medical

information than ever being shared electronically.

Yet, the flip side to this digital transformation is an

increasing threat of data leaks, privacy breaches and

failure to comply with strict data protection

regulations such as the Health Insurance Portability

and Accountability Act (HIPAA) and General Data

Protection Regulation (GDPR). Existing, centralised

healthcare information systems usually have

difficulties achieving the level of transparency,

resilience and trust based on which a high-stake

domain like healthcare can operate.

Owing to its decentralized architecture and

immutable nature, as well as its smart contracting

functionality, the blockchain technology is

considered as a viable solution to the traditional

healthcare data security models. However, this 1

method has suffered from limited clinical

implementation, largely because of challenges in

terms of scalability, integration with legacy systems

and the real-time processing of huge volumes of

Sunkari, V., Bogale, F., Sampoornam, M. M., Sirisha, M. R., A., I. J. J. and Rashid, S. Z.

Blockchain-Integrated Secure Healthcare Ecosystem: A Scalable and Privacy-Compliant Framework for Real-Time Patient Data Protection.

DOI: 10.5220/0013858300004919

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

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

109-116

ISBN: 978-989-758-777-1

109

computerized data. In addition, many of

specifications using blockchain do not meet those of

the complex healthcare workflow organization and

regulation.

In confronting these issues, this study suggests an

entire blockchain-based healthcare data ecosystem

that balances security, privacy and operational

usability. By incorporating smart contracts for the

automation of consent management, guarantee of

interoperability with health information systems

(HISs) with HL7/FHIR, and improvement on the

transaction efficiency by light weight consensus

algorithm, this framework paves the way to a

suitable secure, scalable, and patient-centric model.

This paper not only proposes a strong architecture,

but also provides empirical evidence to the

effectiveness of our architecture by simulating the

real-world clinical data, and the results show that

under the new architecture, the data integrity, privacy

preservation and regulation compliance all achieved

significant improvements. By doing so, a new

standard for secure, intelligent healthcare data

management is imagined, one that can enable both

providers and patients to better manage and protect

their most sensitive information in an ever more

digital world.

2 PROBLEM STATEMENT

The rapid development of digital healthcare has

generated novel challenges to secure sensitive patient

data. Conventional centralized digital databases are

susceptible to cyber-attacks, unauthorized access and

data tampering, which in turn impacts the integrity

and privacy associated with medical records.

Regulatory standards such as HIPAA and GDPR

require strict privacy controls, but current health IT

infrastructure does not facilitate real-time and

auditable enforcement nor patient driven ownership

of data. In addition, existing solutions do not optimize

the balance between security robustness and

operational efficiency and can become latency-prone

and unscalable undesirable for clinical context

implementation staire, the majority of current

implementations do not balance robust security with

operational efficiency, resulting in latency and low

scalability issues in clinical settings. Blockchains

provide a new architecture for secure and

tamperproof data management but so far, few

blockchain-based healthcare systems go beyond

theory, having no validation in practice and no

integration with established legacy systems or

support of typical clinical workflows. Therefore,

there is a pressing demand for an actual, scalable, and

regulation-safe blockchain solution that is able to

store patients' data securely and allow its real-time,

transparent and save access by authorized parties in

the healthcare domain.

3 LITERATURE SURVEY

Security and Privacy of Patient Data in Healthcare

Systems Since the wide-spread of EHR systems, the

security and privacy of patient data in healthcare

systems is a major concern nowadays. Traditional

platforms, which were to a large extent siloed and

centralized, have demonstrated their susceptibility to

breach and malicious manipulation of data, giving

rise to a close survey of decentralized solutions such

as blockchain.

Early works in this area described blockchain-

based solutions only as a theoretical model. For

instance, Azaria et al. (2016) proposed MedRec, a

system that proved the practicality of implementing

blockchain to handle medical records, but failed to

provide actual deployment and integration with

hospitals. Similarly, Yue et al. (2016) discussed

health intelligence on the blockchain with

significance on risk control and inadequate interlinks

with the current health infrastructure.

Later works started to tackle private security

requirements. Chenthara et al. (2020) introduced

HealthChain, a privacy-preserving block chain

model, that was still mainly theoretical. Pandey et al.

(2021) surveyed a wide spectrum of blockchain in

healthcare security, but they did not provide

comparative performance evaluations. Zhang et al.

(2021) reviewed security and privacy structures,

focusing on the theoretical security layers and

application in clinical settings were not performed.

Certain research took blockchain further to

implement smart contracts to handle access control

and consent management (Nguyen et al., 2021;

Meisami et al., 2021), but were challenged by the

issues of scalability and latency. Esposito et al. (2018)

suggested cloud-blockchain hybrid to enhance data

processing but ignored energy and computational

overheads. Similarly, Kuo et al. (2017) addressed the

potential of blockchain in biomedical field and

provided few insights on regulatory issues.

In addition, some of the exisiting solutions such

as those proposed by Radanovic and Likić (2018)

found possible applications of blockchain in medical

records, though they did not develop deployable

architectures. Benchoufi and Ravaud (2017) stressed

the integrity of performing clinical trials with

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

110

blockchain but did not consider patient record

systems and connection with EHR. Omolara et al.

(2020) focused on protecting the privacy through

decoy information, presenting novelty in deception-

based models, and not blockchain-based defenses.

Other authors turned their attention to security

breach and data vulnerability analysis. Hussain Seh et

al. (2020) shed light into the causes and impacts of

healthcare data breaches, further emphasizing the

importance of the immutability property of

blockchain. Thapa and Camtepe (2021) have also

fuelled the above debates by examining the privacy

requirements in precision health systems, and

exposing the deficiencies of existing security

interventions.

Also, work that is legal and policy oriented like

Koch's (2017) and Cohen et al. (2020), and Brinson

& Rutherford (2020) highlighted the regulatory

loophole, notably in the convergence of digital health

records with developing privacy laws. But these did

not have technological backings that would put the

policies into relevant and enforceable systems.

Efforts such as Gropper (2016) and Searls (2016),

although groundbreaking in the design of patient-

driven models, do not provide technically feasible or

scalable systems.

Specific advances in architecture tailored for

blockchain I.9 and / or blockchain I.11 applications

have also been proposed. Zyskind et al. (2015)

suggested personal data storage decentralization with

blockchain, which serves as the foundation for

today’s decentralized healthcare record systems.

Kassab et al. (2021) and Islam et al. (2021) extended

these methodologies by adding data derivation and

fine-grained access control and interoperability, but

saw little real-time clinical use.

Despite these efforts, a major limitation in all

these applications can be attributed to the absence of

a comprehensive, scalable, and regulation-compliant

architecture that can accommodate for efficient

operation in real-time conditions, and allows for

backward compatibility. Hence, this study extends

the basic explorative research to provide a real world

and viable blockchain platform for healthcare, special

concern has been for security, privacy and scalability

for real life deployment; generalizing the platform for

healthcare requirements along with support for

standards such as, GDPR, HIPAA and HL7/FHIR.

4 METHODOLOGY

To overcome the limitations found in current

blockchainbased model for health data security, this

study uses layered approach to provide real-time

operability, privacy compliance, and integration with

existing clinical systems. The methodology for

constructing such a system is to develop a

decentralized system architecture built upon a

private blockchain system instantiated in a

permissioned ledger system, for example a private

blockchain network instantiated using Hyperledger

Fabric. That decision allows for regulated

participation today from known healthcare

organizations like hospitals, diagnostic laboratories,

and insurers, so only legitimate nodes are able to read

from and write to the ledger.

Figure 1: Workflow of the Proposed Blockchain-Based

Healthcare Data Security Framework.

At the heart is a smart contract framework,

tailored for data access policies, patient consent

management, and compliance with regulations such

as HIPAA or GDPR. Such smart contracts record data

transactions, mandate consent rules automatically,

and manage role-based access in real time. Whenever

a healthcare provider or organization requests for

patient’s records, the smart contract verifies

permission before the transaction is approved or

rejected and thus, no need for oversight of a central

authority or internal personnel and no unauthorized

access. Table 1 gives the comparative analysis of

Access Control Models.

Blockchain-Integrated Secure Healthcare Ecosystem: A Scalable and Privacy-Compliant Framework for Real-Time Patient Data Protection

111

Table 1: Comparative Analysis of Access Control Models.

Model

Access

Enforcemen

Real-time

Capability

Patient Consent

Handling

Auditabi

lity

Scalability

Centralized RBAC

Manual

Medium

Limited

Weak

High

Traditional EHR

Systems

Rule-based

High

Minimal

Low

High

Proposed

Blockchain Model

Smart

Contracts

High

Embedded

Strong

High

Figure 1 shows the Workflow of the Proposed

Blockchain-Based Healthcare Data Security

Framework. For the sake of interoperability with

traditional healthcare systems, HL7 FHIR (Fast

Healthcare Interoperability Resources) standards are

also supported by the framework to enable

frictionless data exchange across EHRs, laboratory

systems, and mobile healthcare applications. All

healthcare data could be tokenized first then

encrypted by more complex symmetric encryptions,

we save them off-chain in a fully decentralized file

system (like the one of IPFS) and we store only

metadata and hash references on the blockchain to

make the record immutable and secure.

The suggested system additionally integrates a

light-weight consensus scheme in form of Proof of

Authority (PoA), which enables fast transaction

validation and trust within a healthcare organization

consortium network. This is because the consensus

mechanism is achieved which is several orders of

magnitude less computationally expensive compared

to public blockchains, affording the system in real

time clinical settings free of latency bottlenecks.

Table 2 gives the system configuration and

deployment details.

Table 2: System Configuration and Deployment Details.

Component

Description

Blockchain Framework

Hyperledger Fabric

(v2.x)

Consensus Mechanism

Proof of Authority

(PoA)

Off-Chain Storage

IPFS (InterPlanetary

File System)

Node Deployment

Dockerized on Multi-

Cloud Instances

Data Standardization

HL7 FHIR

Smart Contract Language

GoLang / Solidity

(Fabric chaincode)

The implementation stage consists of the

simulation of the system using real de-identified

healthcare datasets, such as those derived from EHRs,

patient monitoring and insurance claims. Data

preprocessing takes place with identifiers being

anonymized and formats harmonized. The blockchain

nodes are deployed in containerized environments to

resemble the multi-hospital involvement, and smart

contracts are tested for correctness and policy

enforcement.

Performance measures including fetch data delay,

transaction rate, smart contract execution delay, time

for detecting breaches, etc. are obtained based on the

simulation. Moreover, comparison with the state-of-

the-art centralised solutions, and non-blockchain

based Privacy mechanisms, is conducted to prove the

benefits of the proposed model.

Finally, for compliance and auditability, the

system produces detailed logs of accesses and

tamper-evident trails for each access to the data.

These logs are not just a regulatory audit machine,

however; they also empower patients by providing

them with a view of who has accessed their records

and at what time. Leveraging the immutability feature

of the blockchain along with a well-defined

healthcare standard and a focus on usability, the

proposed approach offers a feasible and scalable

mechanism to protect sensitive IA without significant

impact on the digital healthcare landscape.

5 RESULT AND DISCUSSION

The blockchain-based healthcare security dream-

work was studied via a sequence of in situa

simulations with synthetic clinical datasets such as

EHR (electronic health records), diagnostics logs and

patient admission histories. Performance of the

system was evaluated in access related latency,

transaction-based throughput, smart contract s

execution time and breach detection response time.

These performance metrics were compared with a

centralized EHR management model which was fitted

with no blockchain as well as a classical role-based

access control and authorization model.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

112

The most important impact observed was the

decrease of unauthorized accesses to the data. Using

smart contract to realize on-line permission

verification, the system could also automatically

realize the corresponding access control authority.

Any attempt to access data that broke fine-grained

privacy rules and did not fall within the scope of

patient consent was immediately refused and logged,

thus creating an audit trail which was both

tamperproof and indelible. This policy enforcement

on auto greatly increased compliance of data

protection policies ensuring all access attempts, both

internal and external, are auditable. Table 3 gives the

information about the performance metrics

comparison.

Table 3: Performance Metrics Comparison.

Metric

Centralized

EHR

System

Proposed

Blockchai

n System

Avg. Access

Latency (sec)

2.9

1.4

Transaction

Throughput

(TPS)

110

270

Breach

Detection

Response

Delayed

Real-time

Audit Log

Immutability

Weak

Strong

Figure 2: Comparison of Access Latency Between Systems.

The blockchain solution had a steady flow of

transaction processing between 250 and 300

transactions per second (TPS) utilizing Pow of

Authority (PoA) consensus. 2 Public Blockchains

The performance is much higher than that of public

blockchains like Ethereum, and can meet the

requirements of hospital settings in which numerous

nodes (departments or partner hospitals) cooperate to

carry out high-speed data exchanges. The lightweight

PoA consensus was found beneficial for scaling the

system out, meanwhile minimizing the computational

overhead of the consensus (therefore accessible also

medium-sized clinics having a limited)

infrastructure. Figure 3 gives the throughput over

time graph.

Figure 3: Transaction Throughput Over Time.

As shown in figure 2, Latency was also collected

to evaluate the real-time of the system. Typically, the

mean time to access data, between request and

retrieve, was less than 1.5s. This encompasses

running smart contracts, verifying patient consent and

checking metadata hashes on the chain. These

findings illustrate the framework's ability to facilitate

access to urgent-time healthcare functions (e.g.-

emergency room lookups for patient medical history

or real-time probation test lookups during a

consultation). Table 4 states the smart contract

execution results and figure 4 gives the smart contract

access decision.

Blockchain-Integrated Secure Healthcare Ecosystem: A Scalable and Privacy-Compliant Framework for Real-Time Patient Data Protection

113

Table 4: Smart Contract Execution Results.

Use Case Scenario

Contract

Triggere

Execution Time

(ms)

Access

Allowed

Consent

Verified

Doctor requests EHR access

Yes

210

Yes

Insurer requests claim file

Yes

235

Yes

Unauthorized user access

Yes

180

Lab uploads test report

Yes

190

Yes

Not Required

Figure 4: Smart Contract Access Decisions.

In terms of privacy assurance, the use of off-chain

storage (via IPFS) combined with on-chain hash

verification provided a robust defense against data

tampering. Even if an off-chain record was modified

externally, the mismatch between the hash stored on

the blockchain and the recalculated hash flagged the

file as compromised, triggering an alert and blocking

access. This mechanism ensured that only valid and

unchanged files could be accessed through the

system, reinforcing data integrity and auditability.

Furthermore, the system’s integration with HL7

FHIR standards allowed smooth communication with

existing EHR systems. Real-time synchronization of

patient records between blockchain nodes and

hospital databases enabled seamless operation

without disrupting clinical workflows. Physicians and

administrative staff were able to access and share

information across departments and institutions

securely, while patients retained visibility over their

data through blockchain-logged access records. Table

5 gives the User Feedback Summary on Prototype

Usability.

Table 5: User Feedback Summary on Prototype Usability.

Feedback

Category

Satisfaction

Score (1–5)

User Comments

Access

Transparency

4.8

“I can now see

who accessed the

data and why.”

Ease of Use

4.1

“Initially

complex, but

intuitive once

learned.”

Integration with

Workflow

4.5

“Fits well with

existing systems

like EHR.”

Trust in Data

Security

4.9

“It’s great

knowing the

records cannot be

altered.”

Figure 5: User Satisfaction Feedback on Blockchain

System.

As shown in figure 5, a user feedback study was

also conducted with healthcare IT professionals, who

highlighted the transparency of access logs, patient-

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

114

centric design, and system responsiveness as major

improvements over legacy systems. However, some

concerns regarding the learning curve for smart

contract management and initial integration

complexity were raised, which points to the need for

tailored training and modular deployment strategies.

Overall, the results clearly validate the

effectiveness of the proposed system in enhancing

healthcare data security, minimizing unauthorized

access, reducing operational latency, and ensuring

regulatory compliance. Compared to conventional

systems, the blockchain-based framework offers a

substantial leap in terms of auditability, automation,

and trust all of which are critical in today's evolving

healthcare data landscape.

6 CONCLUSIONS

In an era where digital transformation is redefining

healthcare delivery, the security and privacy of

patient data have become critical challenges that

demand innovative solutions. This research has

demonstrated how blockchain technology, when

carefully integrated with smart contracts, off-chain

storage, and healthcare interoperability standards, can

provide a secure, scalable, and regulation-compliant

framework for managing electronic health records.

By addressing the limitations of conventional

centralized systems and overcoming the drawbacks of

existing blockchain models, the proposed framework

ensures real-time access control, immutable logging,

and patient-centric data governance.

The simulation results confirm that the

architecture effectively reduces data breach risks,

minimizes latency, and enforces privacy compliance

through automated smart contract mechanisms. The

incorporation of HL7/FHIR standards further

facilitates seamless integration with legacy health

information systems, making the model practical for

real-world deployment across various healthcare

environments. Beyond technical efficiency, the

system empowers patients by granting visibility and

control over their data, aligning with modern

principles of digital ethics and data ownership.

Ultimately, this work not only validates

blockchain’s potential in safeguarding healthcare

information but also lays the groundwork for its

broader adoption within digital health ecosystems.

Future research may extend this architecture to

support cross-border data sharing, AI-driven

analytics, and federated learning models, enabling

even greater value from secure and decentralized

healthcare infrastructure.

REFERENCES

Angraal, S., Krumholz, H. M., & Schulz, W. L. (2017).

Blockchain technology: Applications in health care.

Circulation: Cardiovascular Quality and Outcomes,

10(9), e003800.

Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016).

MedRec: Using blockchain for medical data access and

permission management. In 2016 2nd International

Conference on Open and Big Data (OBD) (pp. 25-30).

IEEE.

Benchoufi, M., & Ravaud, P. (2017). Blockchain

technology for improving clinical research quality.

Trials, 18(1), 335.

Brinson, N. H., & Rutherford, D. N. (2020). Privacy and the

quantified self: A review of U.S. health information

policy limitations related to wearable technologies.

Journal of Consumer Affairs, 54(3), 1076-1103.

Wikipedia

Chenthara, S., Ahmed, K., Wang, H., Whittaker, F., &

Chen, Z. (2020). Healthchain: A novel framework on

privacy preservation of electronic health records using

blockchain technology. PLOS ONE, 15(12), e0243043.

Wikipedia

Cohen, I. G., Gerke, S., & Kramer, D. B. (2020). Ethical

and legal implications of remote monitoring of medical

devices. The Milbank Quarterly, 98(4), 1090-1128.

Wikipedia

Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo,

K. K. R. (2018). Blockchain: A panacea for healthcare

cloud-based data security and privacy? IEEE Cloud

Computing, 5(1), 31-37.

Gropper, A. (2016). Powering the physician-patient

relationship with HIE of One blockchain health IT. U.S.

Department of Health and Human Services.Wikipedia

Hussain Seh, A., Zarour, M., Alenezi, M., Sarkar, A. K., &

Agrawal, A. (2020). Healthcare data breaches: Insights

and implications. Healthcare, 8(2), 133.Wikipedia

Koch, D. D. (2017). Is the HIPAA Security Rule enough to

protect electronic personal health information (PHI) in

the cyber age? Journal of Health Care Finance, 43(4),

1-10.Wikipedia

Kumar, P., & Lee, H. J. (2021). Security issues in healthcare

applications using wireless medical sensor networks: A

survey. Sensors, 21(1), 1-25.Wikipedia

Kuo, T. T., Kim, H. E., & Ohno-Machado, L. (2017).

Blockchain distributed ledger technologies for

biomedical and health care applications. Journal of the

American Medical Informatics Association, 24(6),

1211-1220.

Meisami, S., Beheshti-Atashgah, M., & Aref, M. R. (2021).

Using blockchain to achieve decentralized privacy in

IoT healthcare. arXiv preprint arXiv:2109.14812.

https://arxiv.org/abs/2109.14812arXiv

Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne,

A. (2021). A cooperative architecture of data offloading

and sharing for smart healthcare with blockchain. arXiv

preprint arXiv:2103.10186. https://arxiv.org/abs/2103.

10186arXiv

Blockchain-Integrated Secure Healthcare Ecosystem: A Scalable and Privacy-Compliant Framework for Real-Time Patient Data Protection

115

Omolara, A. E., Jantan, A., Abiodun, O. I., Arshad, H., &

Dada, K. V. (2020). HoneyDetails: A prototype for

ensuring patient's information privacy and thwarting

electronic health record threats based on decoys. Health

Informatics Journal, 26(1), 3-15.Wikipedia

Pandey, M., Agarwal, R., Shukla, S. K., & Verma, N. K.

(2021). Security of healthcare data using blockchains:

A survey. arXiv preprint arXiv:2103.12326.

https://arxiv.org/abs/2103.12326arXiv

Searls, D. (2016). Consumers can't help health care.

Customers can. Doc Searls Weblog.Wikipedia

Thapa, C., & Camtepe, S. (2021). Precision health data:

Requirements, challenges and existing techniques for

data security and privacy. Computers in Biology and

Medicine, 133, 104387.Wikipedia

Windley, P. (2016). Sovrin use cases: Healthcare. Phil

Windley's Technometria.Wikipedia

Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016).

Healthcare data gateways: Found healthcare intelligen

ce on blockchain with novel privacy risk control.

Journal of Medical Systems, 40(10), 218.

Zhang, R., Xue, R., & Liu, L. (2021). Security and privacy

for healthcare blockchains. arXiv preprint

arXiv:2106.06136. https://arxiv.org/abs/2106.06136

arXiv

Zyskind, G., Nathan, O., & Pentland, A. (2015).

Decentralizing privacy: Using blockchain to protect

personal data. In 2015 IEEE Security and Privacy

Workshops (pp. 180-184). IEEE.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

116