Optimizing Privacy in Federated Learning with MPC and Differential Privacy

With the proliferation of information technologies, data has become a fundamental driver of societal development. However, issues related to data regulation, data rights confirmation, and data protection hinder data circulation, leading to "data islands" and raising concerns about data privacy and security. In this context, achieving a balance between data circulation and privacy protection is crucial. Federated Learning (FL) is a distributed machine learning methodology that allows multiple client devices to collaboratively construct a model without transmitting their local data to a cloud server. However, privacy concerns persist due to potential disclosure of clients' private information through their submitted updates. To mitigate these risks, researchers have integrated differential privacy (DP) techniques into FL, where clients enhance the privacy of their local parameter updates by introducing random noise. This paper presents a novel approach that combines Secure Multi-Party Computation (MPC) with DP to ensure privacy without compromising accuracy. Our approach is resilient to collusion attacks and eliminates potential leakage of any input in the output. Unlike previous solutions, our approach prevents the server from inferring the noisy weights of any specific user, making the system fully private. Our contributions include a more practical assumption of network connectivity and a simplified communication structure of MPC, reducing the required MPC communication overhead and enhancing efficiency. This work provides a significant step towards achieving a balance between data circulation and privacy protection in the era of big data.