  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.
