Blockchain-Based Privacy-Preserving Medical Data Sharing Scheme Using Federated Learning

Abstract

With the booming development of big data technology and health care applications, data in the medical field is characterized by explosive growth, and medical data is valuable, which is the privacy data of patients. However, the characteristics and storage environment of medical big data have brought great challenges to the realization of privacy protection of medical data. In order to ensure the protection of data privacy when sharing medical data, we propose a medical data privacy protection framework based on blockchain (MPBC). In this framework, we protect privacy by adding differential privacy noise into federated learning. In addition, the growing volume of medical data could make blockchain storage problematic. Therefore, a storage mode is proposed to reduce the storage burden of blockchain. The raw data are stored locally and only the hash value calculated by IPFS are stored in blockchain. To enhance the performance, a mechanism is used to validate transactions and aggregate the model. Security analysis shows that our method is a safe and effective way to implement medical data.