A fine-grained medical data sharing scheme based on federated learning

With the rapid development of smart health, the privacy problem of medical data has become more prominent. Aiming at the problem of mining the potential value of medical data and realizing secure sharing, a fine-grained medical data sharing scheme based on federated learning is proposed. The scheme uses collaboration-oriented attribute-based encryption technologies to formulate fine-grained access strategies, allowing medical institutions or doctors to decrypt individually or collaboratively with certain conditions to achieve the purpose of accurately screening the required medical data. In the proposed scheme, the model parameters are shared such that the screened medical data is modeled and analyzed based on federated learning, which allows more people to enjoy top medical resources. In addition, a blockchain-based incentive mechanism is used to reward medical institutions which are either honest with high-quality or helpful in decryption. Hence, the enthusiasm of various medical institutions to screen data and participate in federal learning is improved. Finally, security analysis shows that the scheme is secure, and theoretical analysis and simulation test show the practicability of the scheme.