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Virtual reality (VR) and "metaverse" systems have recently seen a resurgencein interest and investment as major technology companies continue to enter thespace. However, recent studies have demonstrated that the motion tracking"telemetry" data used by nearly all VR applications is as uniquely identifiableas a fingerprint scan, raising significant privacy concerns surroundingmetaverse technologies. Although previous attempts have been made to anonymizeVR motion data, we present in this paper a state-of-the-art VR identificationmodel that can convincingly bypass known defensive countermeasures. We thenpropose a new "deep motion masking" approach that scalably facilitates thereal-time anonymization of VR telemetry data. Through a large-scale user study(N=182), we demonstrate that our method is significantly more usable andprivate than existing VR anonymity systems.

Deep Motion Masking for Secure, Usable, and Scalable Real-Time Anonymization of Virtual Reality Motion Data