Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

【Author】 Rahman, Mohamed Abdur; Hossain, M. Shamim; Islam, Mohammad Saiful; Alrajeh, Nabil A.; Muhammad, Ghulam

【Source】IEEE ACCESS

【Abstract】Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

【Keywords】Blockchain; Internet of Health Things; homomorphic encryption; federated learning; provenance

【标题】安全和来源增强的健康物联网框架：一种区块链管理的联邦学习方法

【摘要】健康物联网 (IoHT) 的最新进展使物联网设备在我们的日常健康管理中得到广泛采用。为了让利益相关者接受物联网数据，除了数据的准确性、安全性、完整性和质量之外，包含物联网的应用程序还必须提供数据来源。为了保护物联网数据的隐私和安全，已经提出了联邦学习（FL）和差分隐私（DP），其中私有物联网数据可以在所有者的场所进行训练。硬件 GPU 的最新进展甚至允许智能手机或边缘设备中的 FL 进程将 IoHT 连接到其边缘节点。尽管 FL 解决了 IoHT 数据的一些隐私问题，但由于所有联合节点缺乏训练能力、高质量训练数据集的稀缺、训练数据的来源以及每个 FL 节点都需要身份验证。在本文中，我们提出了一个轻量级混合 FL 框架，其中区块链智能合约管理参与联合节点的边缘训练计划、信任管理和身份验证、全局或本地训练模型的分布、边缘节点的声誉及其上传的数据集或模型。该框架还支持数据集的完全加密、模型训练和推理过程。每个联合边缘节点执行加法加密，而区块链使用乘法加密来聚合更新的模型参数。为了支持 IoHT 数据的完全隐私和匿名化，该框架支持轻量级 DP。该框架已通过几个专为 COVID-19 患者临床试验而设计的深度学习应用程序进行了测试。我们在此展示了详细的设计、实施和测试结果，这些结果展示了以安全的方式更广泛地采用基于 IoHT 的健康管理的巨大潜力。

【关键词】区块链；健康物联网；同态加密；联邦学习；出处

【发表时间】2020

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】3.476

【翻译者】石东瑛