A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials
<|reference_start|>A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials: The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system.<|reference_end|>
@article{peepliwall2024a,
    title={A Prototype Model of Zero-Trust Architecture Blockchain with
  EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage
  Decentralized Clinical Trials},
    author={Ashok Kumar Peepliwall, Hari Mohan Pandey, Surya Prakash, Anand A
  Mahajan, Sudhinder Singh Chowhan, Vinesh Kumar, Rahul Sharma},
    journal={arXiv preprint arXiv:2408.16885},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16885},
    primaryClass={cs.CR cs.ET cs.IR}
}