# Differential Privacy for Eye-Tracking Data

arXiv: https://arxiv.org/abs/1904.06809 · HTML: https://mlsanity.com/paper/1904.06809/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics](https://mlsanity.com/paper/2201.02110/) (2022)
- [PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation](https://mlsanity.com/paper/2402.18970/) (2024)
- [Smoothed Differential Privacy](https://mlsanity.com/paper/2107.01559/) (2021)
- [QualitEye: Public and Privacy-preserving Gaze Data Quality Verification](https://mlsanity.com/paper/2506.05908/) (2025)
- [HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation](https://mlsanity.com/paper/2111.07015/) (2021)
- [Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder](https://mlsanity.com/paper/2508.10918/) (2025)
- [PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy](https://mlsanity.com/paper/2512.16851/) (2025)
- [Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR](https://mlsanity.com/paper/2609.04592/) (2026)

## Abstract (arXiv)

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking.