Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder

TL;DR

This paper introduces a noise-infused autoencoder that enhances privacy of gaze data by preventing re-identification while maintaining data utility for benign tasks, advancing privacy in gaze-based systems.

Contribution

It proposes a novel latent-noise autoencoder approach that balances privacy and utility in gaze data, outperforming prior methods in preserving physiologically plausible patterns.

Findings

Significantly reduces biometric re-identification

Maintains gaze prediction accuracy with minimal utility loss

Produces physiologically plausible gaze patterns

Abstract

We present a privacy-enhancing mechanism for gaze signals using a latent-noise autoencoder that prevents users from being re-identified across play sessions without their consent, while retaining the usability of the data for benign tasks. We evaluate privacy-utility trade-offs across biometric identification and gaze prediction tasks, showing that our approach significantly reduces biometric identifiability with minimal utility degradation. Unlike prior methods in this direction, our framework retains physiologically plausible gaze patterns suitable for downstream use, which produces favorable privacy-utility trade-off. This work advances privacy in gaze-based systems by providing a usable and effective mechanism for protecting sensitive gaze data.