Computer Graphics

University of California - Berkeley

Unique Identification of 50,000+ Virtual Reality Users from Head and Hand Motion Data


With the recent explosive growth of interest and investment in virtual reality (VR) and the “metaverse,” public attention has rightly shifted toward the unique security and privacy threats that these platforms may pose. While it has long been known that people reveal information about themselves via their motion, the extent to which this makes an individual globally identifiable within virtual reality has not yet been widely understood. In this study, we show that a large number of real VR users (N=55,541) can be uniquely and reliably identified across multiple sessions using just their head and hand motion relative to virtual objects. After training a classification model on 5 minutes of data per person, a user can be uniquely identified amongst the entire pool of 55,541 with 94.33% accuracy from 100 seconds of motion, and with 73.20% accuracy from just 10 seconds of motion. This work is the first to truly demonstrate the extent to which biomechanics may serve as a unique identifier in VR, on par with widely used strong biometrics like facial or fingerprint recognition.


Vivek Nair, Wenbo Guo, Justus Mattern, Rui Wang, James F. O'Brien, Louis Rosenberg, and Dawn Song. "Unique Identification of 50,000+ Virtual Reality Users from Head and Hand Motion Data". In 32nd USENIX Security Symposium (USENIX Security 23), pages 895–910, Anaheim, CA, August 2023. USENIX Association.

Supplemental Material


Talk Slides