PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation

TL;DR

PrivatEyes introduces a privacy-preserving gaze estimation training method using federated learning and secure multi-party computation, effectively protecting user data without sacrificing accuracy or efficiency.

Contribution

It is the first approach to combine federated learning and MPC for appearance-based gaze estimation, enhancing privacy during model training.

Findings

Maintains gaze estimation accuracy comparable to non-secure methods.

Effectively limits private data leakage through DualView attack.

Achieves secure training with minimal additional computational costs.

Abstract

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.