Security and Privacy for Sharing Electronic Medical Records Based on Blockchain and Federated Learning

The sharing of Electronic Medical records (EMRs) has great positive significance for research of disease and doctors' diagnosis. However, patients' EMRs are usually distributed in the databases of multiple medical institutions. Due to the insecurity of the network environment and distrust of other parties, EMR owners worry about data insecurity and privacy leakage, which makes sharing with other parties difficult. Patients worry about the loose control of their health data as well. To solve this problem, we present a solution for the EMRs data sharing based on blockchain and federated learning, which will provide data security and patients' privacy. Firstly, we propose a method for EMRs data retrieval records and sharing records as transaction records adding to the blockchain, and design the two algorithm processes, respectively. Secondly, federated learning is used to help EMRs data owners to build a model based on the original data. The data owner only shares the model instead of the original data. Finally, by security and privacy analytics, we analyzed the advantages and influence of the proposed model. Overall, the evaluation shows that the proposed solution is significantly superior to the previous models and achieves reasonable efficiency for sharing EMRs data.