Personal identifiability of user tracking data during observation of 360-degree VR video

Sci Rep. 2020 Oct 15;10(1):17404. doi: 10.1038/s41598-020-74486-y.


Virtual reality (VR) is a technology that is gaining traction in the consumer market. With it comes an unprecedented ability to track body motions. These body motions are diagnostic of personal identity, medical conditions, and mental states. Previous work has focused on the identifiability of body motions in idealized situations in which some action is chosen by the study designer. In contrast, our work tests the identifiability of users under typical VR viewing circumstances, with no specially designed identifying task. Out of a pool of 511 participants, the system identifies 95% of users correctly when trained on less than 5 min of tracking data per person. We argue these results show nonverbal data should be understood by the public and by researchers as personally identifying data.

MeSH terms

  • Adult
  • Algorithms
  • Female
  • Humans
  • Machine Learning
  • Male
  • Monte Carlo Method
  • Movement*
  • Surveys and Questionnaires
  • Virtual Reality*