# Differential Privacy for Eye Tracking with Temporal Correlations

arXiv: https://arxiv.org/abs/2002.08972 · HTML: https://mlsanity.com/paper/2002.08972/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy](https://mlsanity.com/paper/2402.03907/) (2024)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation](https://mlsanity.com/paper/2402.18970/) (2024)
- [Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies](https://mlsanity.com/paper/2505.07552/) (2025)
- [Towards Everyday Virtual Reality through Eye Tracking](https://mlsanity.com/paper/2203.15703/) (2022)
- [Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study](https://mlsanity.com/paper/2504.10267/) (2025)
- [QualitEye: Public and Privacy-preserving Gaze Data Quality Verification](https://mlsanity.com/paper/2506.05908/) (2025)
- [PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy](https://mlsanity.com/paper/2512.16851/) (2025)
- [Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs](https://mlsanity.com/paper/2604.13243/) (2026)
- [Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces](https://mlsanity.com/paper/2609.09188/) (2026)

## Abstract (arXiv)

New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.