Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
Source abstract
Abstract
An automatic medical diagnosis service based on deep learning has been introduced in e-healthcare, bringing great convenience to human life. However, due to privacy regulations, insufficient data sharing among medical centers has led to many severe challenges for automated medical diagnostic services, including diagnostic accuracy. To solve such problems, swarm learning (SL), a blockchain-based federated learning (BCFL), has been proposed. Although SL avoids single-point-of-failure attacks and offers an incentive mechanism, it still faces privacy breaches and poisoning attacks. In this paper, we propose a new privacy-preserving Byzantine-resilient swarm learning (PBSL) that is resistant to poisoning attacks while protecting data privacy. Specifically, we adopt threshold fully homomorphic encryption (TFHE) to protect data privacy and provide secure aggregation. And the cosine similarity is used to judge the malicious gradient uploaded by malicious medical centers. Through security analysis, PBSL is able to defend against a variety of known security attacks. Finally, PBSL is implemented by uniting deep learning with blockchain-based smart contract platforms. Experiments based on different datasets show that the PBSL algorithm is practical and efficient.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Brain Tumor Detection and Classification · Face and Expression Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
School of Cyber Engineering, Xidan University, Xi’an 710126, China
References (59)
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