Effect of Duration and Delay on the Identifiability of VR Motion
<|reference_start|>Effect of Duration and Delay on the Identifiability of VR Motion: Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments.<|reference_end|>
@article{miller2024effect,
    title={Effect of Duration and Delay on the Identifiability of VR Motion},
    author={Mark Roman Miller, Vivek Nair, Eugy Han, Cyan DeVeaux, Christian Rack,
  Rui Wang, Brandon Huang, Marc Erich Latoschik, James F. O'Brien, Jeremy N.
  Bailenson},
    journal={arXiv preprint arXiv:2407.18380},
    year={2024},
    doi={10.1109/WoWMoM60985.2024.00023},
    archivePrefix={arXiv},
    eprint={2407.18380},
    primaryClass={cs.CR cs.HC}
}