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Related Experiment Video

Updated: Nov 12, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A privacy-preserving approach to streaming eye-tracking data.

Brendan David-John, Diane Hosfelt, Kevin Butler

    IEEE Transactions on Visualization and Computer Graphics
    |March 22, 2021
    PubMed
    Summary

    Eye-tracking in mixed reality risks user identification. A new framework reduces identification rates from 85% to 30% while maintaining gaze prediction accuracy.

    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Virtual Reality

    Background:

    • Eye-tracking technology is increasingly integrated into mixed reality (MR) devices.
    • This integration enables critical applications but poses significant privacy risks.
    • Unique user identification is possible even under natural viewing conditions in virtual reality (VR).

    Purpose of the Study:

    • To assess the risk of unique user identification via eye-tracking data in MR.
    • To propose and evaluate a framework for mitigating privacy risks associated with eye-tracking data.
    • To ensure privacy-by-design principles are applied to eye-tracking data flow in MR.

    Main Methods:

    • Developing a framework incorporating API gatekeeping and software-implemented privacy mechanisms.

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  • Evaluating the effectiveness of the proposed mechanisms in reducing user identification rates.
  • Measuring the impact of privacy mechanisms on gaze prediction accuracy.

    • Main Results:

    • Eye-tracking data in VR presents an appreciable risk of unique user identification.
    • The proposed framework reduced identification rates from 85% to 30%.
    • The privacy mechanisms introduced less than 1.5° error in gaze position for gaze prediction.

    Conclusions:

    • User identification via eye-tracking in MR is a significant privacy concern.
    • The developed framework effectively mitigates privacy risks without compromising essential functionalities like gaze prediction.
    • This approach establishes the first privacy-by-design solution for eye-tracking data in MR use cases.