Personal Identifiability of User Tracking Data During VR Training
Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi · 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · 2021
Recent research indicates that user tracking data from virtual reality (VR) experiences can be used to personally identify users at accuracies as high as 95 percent. However, these results indicating that non-verbal data should be understood as personally identifying data were based on observing 360-degree videos. In this paper, we present participant identification results based on a session of user tracking data from a VR training application, which show accuracies above 90 percent. While still highly accurate, this decrease indicates that the personal identifiability of user tracking data is likely dependent upon the nature of the underlying VR experience.