A zero-knowledge proof federated learning on DLT for healthcare data
【Author】 Petrosino, Lorenzo; Masi, Luigi; D'Antoni, Federico; Merone, Mario; Vollero, Luca
【Source】JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
【影响因子】4.542
【Abstract】With the increasingly widespread adoption of Healthcare 4.0 practices, new challenges have arisen for the utilization of collected sensitive data. On the one hand, these data have immense potential to unlock valuable insights for personalized medicine, early disease detection, and predictive analysis thanks to the use of Artificial Intelligence. On the other hand, ensuring the protection of patient privacy is of paramount importance to maintain trust and uphold ethical practices within the healthcare system. Classical centralized learning approaches do not fit well with the privacy and security requirements imposed by the law and the sensitivity of treated data, which is why decentralized learning approaches are gaining ground. Among these, Federated Learning (FL) stands out as a viable alternative, providing greater security and performance comparable to classic centralized learning approaches. However, there are still various attacks targeting the local parameters or gradients updated by the participants. Therefore, we present our architecture based on the conjunction of Zero-Knowledge Proof, FL, and blockchain that implements also the decentralized identifier standard. The adoption of this architecture can grant the execution, management, supervision, and updating of the FL process, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the performance, robustness, and implementation costs of the proposed architecture, we develop a case study on the prediction of blood glucose levels in people with Type-1-diabetes. The results of our analysis show an improved system in terms of balance between performance privacy and security, guaranteeing high levels of verifiability, therefore proving the proposed architecture suitable for most of the FL processes needed in the healthcare field.
【Keywords】Federated learning; Distributed ledger technology; Zero-knowledge proof; Data security; Decentralized identifiers; Healthcare 4.0; Diabetes
【发表时间】2025 FEB
【收录时间】2024-10-31
【文献类型】理论模型
【主题类别】
区块链技术-协同技术-零知识证明
Zach
这篇论文研究了一种结合零知识证明、联邦学习（FL）和区块链技术的架构，以解决在医疗保健4.0实践中使用收集的敏感数据所面临的新挑战。该架构旨在利用人工智能解锁个性化医疗、早期疾病检测和预测分析的巨大潜力，同时确保患者隐私保护，以维持医疗系统中的信任和伦理实践。传统的集中式学习方法无法满足法律和敏感数据处理的隐私和安全要求，因此去中心化学习方法，特别是FL，作为一种可行的替代方案，提供了更高的安全性和与传统集中式学习方法相当的性能。然而，FL仍面临各种针对参与者更新的本地参数或梯度的攻击。为此，研究人员提出了一种基于零知识证明、FL和区块链的架构，并实施了去中心化标识符标准。该架构能够执行、管理和监督FL过程的更新，确保系统的弹性和交换数据的可追溯性和可靠性。为了测试所提出架构的性能、鲁棒性和实施成本，研究人员开发了一个关于预测1型糖尿病患者血糖水平的案例研究。分析结果显示，该架构在性能、隐私和安全之间实现了更好的平衡，并保证了高水平的可验证性，证明了该架构适用于医疗保健领域中大多数FL过程。
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