A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials
- URL: http://arxiv.org/abs/2408.16885v1
- Date: Thu, 29 Aug 2024 20:18:00 GMT
- Title: A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials
- Authors: Ashok Kumar Peepliwall, Hari Mohan Pandey, Surya Prakash, Anand A Mahajan, Sudhinder Singh Chowhan, Vinesh Kumar, Rahul Sharma,
- Abstract summary: This paper proposes a prototype model of the Zero-Trust Architecture (z-TAB) to integrate patient-generated clinical trial data during DCT operation management.
The Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms.
- Score: 5.565144088361576
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning [0.3789223497926791]
Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts.<n>The proposed solution is ICBAC, an intelligent contract-based access control framework.<n>It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL)
arXiv Detail & Related papers (2026-02-08T15:27:58Z) - Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials [0.06372261626436676]
This paper proposes a Trustworthy-based Federated Learning (TBFL) framework integrating Self-Sovereign Identity (SSI) standards.<n>Our results show the framework successfully neutralizes 100% of Sybil attacks, robust predictive performance, and introduces negligible computational overhead.<n>The approach provides a secure, scalable, and economically viable ecosystem for inter-institutional health data collaboration.
arXiv Detail & Related papers (2026-02-02T17:45:58Z) - zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy [0.0]
zkFL-Health is an architecture that combines Federated Learning (FL) with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs)<n>Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host.<n>We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost.
arXiv Detail & Related papers (2025-12-24T08:29:28Z) - Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis [39.89241412792336]
We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning agents.<n>Our findings indicate that attackers with access to only 100-500 samples can compromise healthcare AI regardless of dataset size.<n>We recommend multilayer defenses including required adversarial testing, ensemble-based detection, privacy-preserving security mechanisms, and international coordination on AI security standards.
arXiv Detail & Related papers (2025-11-14T07:16:16Z) - Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
arXiv Detail & Related papers (2025-05-26T06:44:31Z) - Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT [0.0]
Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has exposed them to significant cybersecurity threats.<n>Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure.<n>We propose a novel Federated Learning-Enhanced Framework (FL-BCID) for privacy-preserving intrusion detection tailored for IIoT environments.
arXiv Detail & Related papers (2025-05-21T11:11:44Z) - Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies.
By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
arXiv Detail & Related papers (2025-04-22T09:01:55Z) - Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification [0.0]
This paper introduces a novel framework that integrates federated learning with quantum-inspired encryption techniques for dementia classification.
The framework offers significant implications for democratizing access to AI-driven dementia diagnostics in low- and middle-income countries.
arXiv Detail & Related papers (2025-03-05T08:49:31Z) - Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [46.404531555921906]
We propose an architecture for blockchain-based PAISs aimed at preserving both confidentiality and transparency.
Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.
arXiv Detail & Related papers (2024-12-07T20:18:36Z) - Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions.
This paper proposes a digital twin (DT) and federated digital twin (FL) scheme.
The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
arXiv Detail & Related papers (2024-11-04T17:48:02Z) - SPOQchain: Platform for Secure, Scalable, and Privacy-Preserving Supply Chain Tracing and Counterfeit Protection [46.68279506084277]
This work proposes SPOQchain, a novel blockchain-based platform that provides comprehensive traceability and originality verification.
It provides an analysis of privacy and security aspects, demonstrating the need and qualification of SPOQchain for the future of supply chain tracing.
arXiv Detail & Related papers (2024-08-30T07:15:43Z) - Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework [1.3654846342364306]
The study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes.
The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility.
arXiv Detail & Related papers (2024-05-19T15:15:18Z) - Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive Approach [0.0]
This study explores the potential of blockchain in enhancing data integrity and traceability within Industry Cyber-Physical Systems (ICPS)
ICPS is pivotal in managing critical infrastructure like manufacturing, power grids, and transportation networks.
This research unearths various blockchain applications in ICPS, including supply chain management, quality control, contract management, and data sharing.
arXiv Detail & Related papers (2024-05-08T06:22:37Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - A Scalable Multi-Layered Blockchain Architecture for Enhanced EHR Sharing and Drug Supply Chain Management [3.149883354098941]
This article presents an innovative Electronic Health Records (EHR) sharing and drug supply chain management framework.
The framework introduces five layers and transactions, prioritizing patient-centric healthcare by granting patients comprehensive access control over their health information.
It provides transparency and real-time drug supply monitoring, empowering decision-makers with actionable insights.
arXiv Detail & Related papers (2024-02-27T09:20:16Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - Blockchain Driven Privacy Preserving Contact Tracing Framework in
Pandemics [8.118795972635452]
Contact tracing is an effective approach to control the virus spread in pandemics like COVID-19 pandemic.
As an emerging powerful decentralized technique, blockchain has been explored to ensure data privacy and security in contact tracing processes.
In this paper, we propose a light-weight and fully third-party free-weight and fully decentralized RSA-Driven Contact Tracing framework (BDCT) to bridge the gap.
arXiv Detail & Related papers (2022-02-18T19:54:16Z) - TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19
Contact Tracing [9.286934094368812]
The COVID-19 pandemic has significantly increased the dependence on Contact Tracing (CT) models.
This paper proposes the Trustworthy-enabled system for Indoor Contact Tracing (TB-ICT) framework.
The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access.
arXiv Detail & Related papers (2021-08-09T17:27:49Z) - Lightweight Collaborative Anomaly Detection for the IoT using Blockchain [40.52854197326305]
Internet of things (IoT) devices tend to have many vulnerabilities which can be exploited by an attacker.
Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner.
We present a distributed IoT simulation platform, which consists of 48 Raspberry Pis.
arXiv Detail & Related papers (2020-06-18T14:50:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.