Personal Identifiability and Obfuscation of User Tracking Data From VR Training Sessions

Alec G. Moore, Ryan P. McMahan, Hailiang Dong, Nicholas Ruozzi

Abstract: Recent research indicates that user tracking data from virtual reality (VR) experiences can be used to personally identify users with degrees of accuracy as high as 95%. However, these results indicating that VR tracking data should be understood as personally identifying data were based on observing 360° videos. In this paper, we present results based on sessions of user tracking data from an ecologically valid VR training application, which indicate that the prior claims may not be as applicable for identifying users beyond the context of observing 360° videos. Our results indicate that the degree of identification accuracy notably decreases between VR sessions. Furthermore, we present results indicating that user tracking data can be obfuscated by encoding positional data as velocity data, which has been successfully used to predict other user experience outcomes like simulator sickness and knowledge acquisition. These results, which show identification accuracies were reduced by more than half, indicate that velocity-based encoding can be used to reduce identifiability and help protect personal identifying data.