FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption

Ensuring data privacy is a significant challenge for machine learning applications, not only during model training but also during evaluation. Federated learning has gained significant research interest in recent years as a result. Current research on federated learning primarily focuses on preserving privacy during the training phase. However, model evaluation has not been adequately addressed, despite the potential for significant privacy leaks during this phase as well. In this paper, we demonstrate that the state-of-the-art AUC computation method for federated learning systems, which utilizes differential privacy, still leaks sensitive information about the test data while also requiring a trusted central entity to perform the computations. More importantly, we show that the performance of this method becomes completely unusable as the data size decreases. In this context, we propose an efficient, accurate, robust, and more secure evaluation algorithm capable of computing the AUC in horizontal federated learning systems. Our approach not only enhances security compared to the current state-of-the-art but also surpasses the state-of-the-art AUC computation method in both approximation performance and computational robustness, as demonstrated by experimental results. To illustrate, our approach can efficiently calculate the AUC of a federated learning system involving 100 parties, achieving 99.93 just 0.68 seconds, regardless of data size, while providing complete data privacy.