Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Ecological Virtual Reality Motion Data
Abstract
Virtual reality (VR) and “metaverse” systems have recently seen a resurgence in interest and investment as major technology companies continue to enter the space. However, recent studies have demonstrated that the motion tracking “telemetry” data used by nearly all VR applications is as uniquely identifiable as a fingerprint scan, raising significant privacy concerns surrounding metaverse technologies. In this paper, we propose a new “deep motion masking” approach that scalably facilitates the real-time anonymization of VR telemetry data. Through a large-scale user study (N=182), we demonstrate that our method is significantly more usable and private than existing VR anonymity systems
Citation
Vivek Nair, Wenbo Guo, James F. O'Brien, Louis Rosenberg, and Dawn Song. "Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Ecological Virtual Reality Motion Data". In IEEE Conference on Virtual Reality and 3D User Interfaces, pages 493–500. IEEE Computer Society, March 2024.
Supplemental Material
Code
Source code for Deep Motion Masking