  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. Although previous attempts have been made to anonymize
VR motion data, we present in this paper a state-of-the-art VR identification
model that can convincingly bypass known defensive countermeasures. We then
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.
