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

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

doi:10.1109/ACCESS.2020.3037474

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

IEEE Access. 2020 Nov 11:8:205071-205087. doi: 10.1109/ACCESS.2020.3037474. eCollection 2020.

Authors

Mohamed Abdur Rahman¹, M Shamim Hossain², Mohammad Saiful Islam³, Nabil A Alrajeh⁴, Ghulam Muhammad⁵

Affiliations

¹ Department of Cyber Security and Forensic ComputingCollege of Computing and Cyber SciencesUniversity of Prince MugrinMadinah41499Saudi Arabia.
² Department of Software EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia.
³ Department of Computing, GoldsmithUniversity of LondonLondonSE14 6NWU.K.
⁴ Department of Biomedical EngineeringCollege of Applied Medical SciencesKing Saud UniversityRiyadh11543Saudi Arabia.
⁵ Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia.

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; federated learning; homomorphic encryption; provenance.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Grants and funding

This work is supported by the Deputyship for Research and Innovation, Ministry of Education, Saudi Arabia for funding through the project number IFKSURG-228.