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

arXiv:2403.14428v1 Announce Type: new
Abstract: 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 …

applications arxiv challenge cs.cr current data data privacy encryption evaluation federated federated learning fully homomorphic encryption homomorphic encryption interest machine machine learning model training privacy privacy preserving research result training