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

• Published in: 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) pp. 493 - 500
• Main Authors: Nair, Vivek, Guo, Wenbo, O'Brien, James F., Rosenberg, Louis, Song, Dawn
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.03.2024
• Subjects: Augmented Reality; Biometrics; Data privacy; Deep learning; Extended Reality; Metaverse; Mixed Reality; MoCap; Motion Capture; Privacy; Real-time systems; Security; Telemetry; Tracking; User interfaces; Virtual reality
• DOI: 10.1109/VRW62533.2024.00096

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.