# Assessing the Privacy Risk of Cross-Platform Identity Linkage using Eye Movement Biometrics

arXiv: https://arxiv.org/abs/2402.08655 · HTML: https://mlsanity.com/paper/2402.08655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR](https://mlsanity.com/paper/2609.04592/) (2026)
- [Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces](https://mlsanity.com/paper/2609.09188/) (2026)

## Abstract (arXiv)

The recent emergence of ubiquitous, multi-platform eye tracking has raised user privacy concerns over re-identification across platforms, where a person is re-identified across multiple eye tracking-enabled platforms using personally identifying information that is implicitly expressed through their eye movement. We present an empirical investigation quantifying a modern eye movement biometric model's ability to link subject identities across three different eye tracking devices using eye movement signals from each device. We show that a state-of-the art eye movement biometrics model demonstrates above-chance levels of biometric performance (34.99% equal error rate, 15% rank-1 identification rate) when linking user identities across one pair of devices, but not for the other. Considering these findings, we also discuss the impact that eye tracking signal quality has on the model's ability to meaningfully associate a subject's identity between two substantially different eye tracking devices. Our investigation advances a fundamental understanding of the privacy risks for identity linkage across platforms by employing both quantitative and qualitative measures of biometric performance, including a visualization of the model's ability to distinguish genuine and imposter authentication attempts across platforms.