[formula omitted]: Self-sovereign identity based privacy-preserving federated learning

Abstract

Traditional federated learning (FL) raises security and privacy concerns such as identity fraud, data poisoning attacks, membership inference attacks, and model inversion attacks. In the conventional FL, any entity can falsify its identity and initiate data poisoning attacks. Besides, adversaries (AD) holding the updated global model parameters can retrieve the plain text of the dataset by initiating membership inference attacks and model inversion attacks. To the best of our knowledge, this is the first work to propose a self-sovereign identity (SSI) and differential privacy (DP) based FL namely SSI−FL for addressing all the above issues. The first step in the SSI−FL framework involves establishing a secure connection based on blockchain-based SSI. This secure connection protects against unauthorized access attacks of any AD and ensures the transmitted data's authenticity, integrity, and availability. The second step applies DP to protect against model inversion attacks and membership inference attacks. The third step focuses on establishing FL with a novel hybrid deep learning to achieve better scores than conventional methods. The SSI−FL performance analysis is done based on security, formal, scalability, and score analysis. Moreover, the proposed method outperforms all the state-of-art techniques.