A LIGHTWEIGHT PROOF-OF-STAKE COMPLIANCE ENFORCEMENT MODEL FOR DATA PRIVACY PRESERVATION IN TELEMEDICINE SYSTEMS

Abstract

Telemedicine expands access to medical care but also generates highly sensitive patient data, creating significant privacy challenges. Existing solutions face three key gaps: limited computational resources that hinder the use of conventional privacy￾preserving techniques on telemedicine devices; existing Proof-of-Stake (PoS) models, such as Algorand and Ouroboros, are not optimized for telemedicine environments; and HIPAA and GDPR requirements are not directly enforced at the transaction level, leaving consent violations and unauthorized access undetected. To address these challenges, this study aimed to develop a lightweight PoS compliance enforcement model to preserve data privacy in telemedicine systems. The specific objectives were to analyze the features of lightweight PoS models relevant to preserving data privacy; to develop a lightweight PoS voting mechanism based on identified features; to develop a lightweight PoS model incorporating the proposed voting mechanism to enforce data privacy compliance; and to evaluate the performance and effectiveness of the proposed model. The study employs a pragmatic research philosophy and Design Science Research (DSR) design, utilizing both simulated telemedicine device data and 4,999 data points from Kaggle (MTSample) for empirical validation. The study identified four core features of lightweight PoS: voting mechanism, cryptography, storage, and architecture. A voting mechanism based on Byzantine Agreement and cluster pre-consensus, combined with cryptography, was developed to reduce message complexity. A privacy compliance model (M_LPoS) that integrates voting with the Local Privacy Decision Function (LPDF) for regulation was also created. Performance tests showed that M_LPoS achieved high throughput, low latency, and a privacy compliance score exceeding HIPAA’s 75% threshold, while reducing energy use compared against baseline models. M-LPoS optimizes computational efficiency, ensures equitable reward distribution, and embeds privacy within the architectural mechanism. This makes it ideally suited to resource￾constrained telemedicine systems. The study recommends that healthcare providers and telemedicine systems vendors adopt the proposed Lightweight PoS compliance enforcement model (M-LPoS) as a foundational privacy layer in their telemedicine platforms and implement energy-efficient processes. At the same time, policymakers should consider updating privacy standards to incorporate lightweight blockchain technologies. Future studies should validate the model using real-time telemedicine data and conduct longitudinal evaluations of stakeholder participation, decentralized governance, economic viability, and regulatory compliance. Also, further studies should focus on developing adaptive clustering algorithms and cross-jurisdictional compliance to facilitate the worldwide implementation of telemedicine.