Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience
<|reference_start|>Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience: Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios.   This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions.<|reference_end|>
@article{wilson2024privacy-preserving,
    title={Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual
  Reality: Robustness and User Experience},
    author={Ethan Wilson, Azim Ibragimov, Michael J. Proulx, Sai Deep Tetali,
  Kevin Butler, Eakta Jain},
    journal={arXiv preprint arXiv:2402.07687},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07687},
    primaryClass={cs.HC cs.CR}
}