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<p>The Internet of Healthcare Things (IoHT) has transformed healthcare by enabling real-time data collection and remote monitoring. However, it faces critical challenges in preserving data privacy, ensuring fault tolerance, and preventing forgery. Existing blockchain-based solutions enhance security but fall short in maintaining user anonymity and are susceptible to Sybil and collusion attacks. This paper addresses three key research gaps: (1) insufficient user anonymity, (2) vulnerability to network attacks, and (3) inefficiencies in data sharing within resource-constrained environments. To overcome these challenges, we propose a novel framework that integrates a zk-SNARK-driven pseudonym shuffling mechanism for enhanced anonymity, a trust-based pseudonym consensus algorithm to resist Sybil and collusion attacks, and a Variational Autoencoder (VAE) based storage solution for forgery detection. The proposed system ensures secure healthcare data management while improving computational efficiency and compliance with privacy regulations. Performance analysis demonstrates significantly improved latency, scalability, and forgery detection accuracy compared to state-of-the-art methods. Security evaluations further validate the framework’s robustness against adversarial attacks, making it a scalable and privacy-preserving solution for decentralized healthcare systems.</p>

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Pseudonym shuffling-driven blockchain and Autoencoder-based secure E-healthcare data management

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Tanuj Chandela,
  • Ayush Verma,
  • Geetanjali Rathee,
  • Abhinav Tomar,
  • Gaurav Singal

摘要

The Internet of Healthcare Things (IoHT) has transformed healthcare by enabling real-time data collection and remote monitoring. However, it faces critical challenges in preserving data privacy, ensuring fault tolerance, and preventing forgery. Existing blockchain-based solutions enhance security but fall short in maintaining user anonymity and are susceptible to Sybil and collusion attacks. This paper addresses three key research gaps: (1) insufficient user anonymity, (2) vulnerability to network attacks, and (3) inefficiencies in data sharing within resource-constrained environments. To overcome these challenges, we propose a novel framework that integrates a zk-SNARK-driven pseudonym shuffling mechanism for enhanced anonymity, a trust-based pseudonym consensus algorithm to resist Sybil and collusion attacks, and a Variational Autoencoder (VAE) based storage solution for forgery detection. The proposed system ensures secure healthcare data management while improving computational efficiency and compliance with privacy regulations. Performance analysis demonstrates significantly improved latency, scalability, and forgery detection accuracy compared to state-of-the-art methods. Security evaluations further validate the framework’s robustness against adversarial attacks, making it a scalable and privacy-preserving solution for decentralized healthcare systems.