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

Mohamed Abdur Rahman,Mohammad Saiful Islam,Ghulam Muhammad,Nabil A Alrajeh,M Shamim Hossain

doi:10.1109/access.2020.3037474

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.

Highlights

With the availability of the Internet of Health Things (IoHT), more health data is becoming available for the healthcare industry to benefit from [1] [2]
To add an extra layer of security at the blockchain nodes that are responsible for aggregating the gradients, we propose using the Intel Software Guard Extensions (SGX) trusted execution environment (TEE), which will use a secure enclave for the local model aggregation process
The differentially private IoHT raw data is first stored in the InterPlanetary File System (IPFS) repository, and the hash of the training data or model location from the IPFS is stored in the blockchain for provenance and collaborative model training

Summary

INTRODUCTION

With the availability of the Internet of Health Things (IoHT), more health data is becoming available for the healthcare industry to benefit from [1] [2]. This has led to deep learning framework and library designers such as Google and Facebook offering frameworks such as TensorFlow Encrypted, Syft Keras, and PyTorch Opacus These models allow both the private data owner and the external model owner to encrypt their data and model respectively and perform secure training and model inferencing in a distributed environment, without needing to trust any particular entity. Another approach suggested by researchers in [39] uses differentially private generative adversarial network (GAN) to generate secret tokens for detecting malicious attackers, and differentially private stochastic gradient descent to handle privacy leakage.

LITERATURE REVIEW

SYSTEM DESIGN

IMPLEMENTATION

Findings

CONCLUSION