GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning

Abstract

Privacy must be preserved when working with client data in machine learning. Federated learning (FL) provides a way to preserve user data privacy by aggregating locally trained models without sharing the user data. Still, the privacy of user identity is not preserved. Secure aggregation is a popular technique in FL for aggregating gradients without disclosing individual data. However, it is costly and inaccurate. Therefore, we propose a novel, scalable, cost-effective group signature-based secure aggregation algorithm in FL, called GSFedSec, where secure aggregation helps conceal the user’s update while the group signature helps conceal their identity. Our algorithm preserves the data and their source. Our simulation results show that the proposed algorithm does not suffer from a loss of accuracy, handles increases in network size competently, offers computational and communication efficiency, and is secure against various security attacks. We have compared the results of efficiency and security against existing algorithms in FL. Also, the security of the algorithm is verified using Burrows–Abadi–Needham (BAN) logic and simulated via the Automated Validation of Internet Security Protocols and Applications (AVISPA) protocol.

Keywords

• data privacy
• user privacy
• federated learning
• secure aggregation
• efficiency
• group signature