A Hybrid Encryption Medical Information Security Collaboration Scheme Based on Blockchain and Federated Learning

¹ 1 School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, Shanxi 030619, China 2 Shanxi Key Laboratory of Intelligent Optimization Computation and Blockchain Technology, Taiyuan Normal University, Jinzhong, Shanxi 030619, China,

JERA 2025 , 9(6), 443–451; https://doi.org/10.26689/jera.v9i6.12502

© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

In current medical data sharing practices, the tension between data privacy protection and cross-institutional collaboration efficiency has become increasingly prominent. To address existing security challenges in healthcare data sharing, we propose a collaborative data cooperation model based on blockchain and federated learning, Through federated learning technology, data is made “usable but not visible” by enabling medical institutions to share only encrypted model parameters, thereby preventing the leakage of raw data. Meanwhile, blockchain technology is introduced to establish a decentralized trust mechanism, utilizing smart contracts to automate data access management and track training processes. In addition, the dual security protection strategy is designed, where differential privacy and Paillier homomorphic encryption technology are adopted to resist member reasoning attacks and ensure secure storage and sharing of information. Through security analysis and experimental validation, the scheme has been proven to have good security and usability.

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