# Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder

arXiv: https://arxiv.org/abs/2508.10918 · HTML: https://mlsanity.com/paper/2508.10918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images](https://mlsanity.com/paper/1712.00321/) (2017)
- [GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset](https://mlsanity.com/paper/2009.06171/) (2020)
- [A privacy-preserving approach to streaming eye-tracking data](https://mlsanity.com/paper/2102.01770/) (2021)
- [Privacy-Aware Eye Tracking Using Differential Privacy](https://mlsanity.com/paper/1812.08000/) (2018)
- [Differential Privacy for Eye-Tracking Data](https://mlsanity.com/paper/1904.06809/) (2019)
- [Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation](https://mlsanity.com/paper/2205.01950/) (2022)
- [Privacy-Preserving Autoencoder for Collaborative Object Detection](https://mlsanity.com/paper/2402.18864/) (2024)
- [Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset](https://mlsanity.com/paper/2404.11798/) (2024)
- [Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience](https://mlsanity.com/paper/2402.07687/) (2024)
- [Adjustable Privacy using Autoencoder-based Learning Structure](https://mlsanity.com/paper/2304.03538/) (2023)

## Abstract (arXiv)

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.