A PRIVACY-AWARE ENHANCEMENT OF PROOF OF ELAPSED TIME (PPOET) FOR HEALTHCARE BLOCKCHAIN SYSTEMS

ICTACT Journal on Data Science and Machine Learning ( Volume: 7 , Issue: 4 )

Abstract

The healthcare sector faces persistent challenges in ensuring data security, privacy, and regulatory compliance, as traditional systems remain vulnerable to breaches and unauthorized access. Blockchain technology offers decentralization, immutability, and transparency. Its effective adoption in healthcare requires consensus mechanisms tailored to domain-specific requirements. Existing approaches such as Proof of Elapsed Time (PoET) provide scalability and energy efficiency but offer limited support for privacy awareness and healthcare-oriented data management. This paper presents a privacy-aware enhancement of the Proof of Elapsed Time (PPoET) consensus mechanism for healthcare blockchain systems. The proposed approach introduces asynchronous wait-time generation to reduce latency and improve system responsiveness, along with ECDSA-based authentication and SHA-512 hashing to strengthen transaction integrity and trust. Unlike conventional PoET implementations that rely on hardware-based Trusted Execution Environments (TEE), the proposed model adopts a software-based cryptographic approach, enabling practical deployment without specialized hardware. Experimental results demonstrate that PPoET achieves reduced transaction latency and stable resource utilization while maintaining acceptable throughput under healthcare workloads. The proposed framework supports secure and efficient management of Electronic Health Records (EHRs) and provides a scalable and reliable solution for healthcare blockchain applications.

Authors

S. Kanagasankari, J. Rexy, M. Preethi, T. Idhaya
St. Xavier’s College, India

Keywords

Blockchain, Consensus Mechanism, Proof of Elapsed Time (PoET), Privacy-Aware PPoET, Elliptic Curve Digital Signature Algorithm (ECDSA), Electronic Health Records (EHR), SHA-512

Published By
ICTACT
Published In
ICTACT Journal on Data Science and Machine Learning
( Volume: 7 , Issue: 4 )
Date of Publication
September 2026
Pages
1181 - 1191
Page Views
115
Full Text Views
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