PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation
This addresses privacy concerns for users in gaze estimation applications, though it is incremental as it builds on existing federated learning and secure computation techniques.
The paper tackles the privacy risks in collecting and exchanging large-scale training data for gaze estimation by proposing PrivatEyes, a method using federated learning and secure multi-party computation, which maintains privacy against malicious servers without compromising accuracy or computational costs compared to non-secure methods.
Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on multiple local datasets across different users and server-based secure aggregation of the individual estimators' updates. PrivatEyes guarantees that individual gaze data remains private even if a majority of the aggregating servers is malicious. We also introduce a new data leakage attack DualView that shows that PrivatEyes limits the leakage of private training data more effectively than previous approaches. Evaluations on the MPIIGaze, MPIIFaceGaze, GazeCapture, and NVGaze datasets further show that the improved privacy does not lead to a lower gaze estimation accuracy or substantially higher computational costs - both of which are on par with its non-secure counterparts.