  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.
