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Motion tracking "telemetry" data lies at the core of nearly all modernvirtual reality (VR) and metaverse experiences. While generally presumedinnocuous, recent studies have demonstrated that motion data actually has thepotential to uniquely identify VR users. In this study, we go a step further,showing that a variety of private user information can be inferred just byanalyzing motion data recorded from VR devices. We conducted a large-scalesurvey of VR users (N=1,006) with dozens of questions ranging from backgroundand demographics to behavioral patterns and health information. We thenobtained VR motion samples of each user playing the game "Beat Saber," andattempted to infer their survey responses using just their head and hand motionpatterns. Using simple machine learning models, over 40 personal attributescould be accurately and consistently inferred from VR motion data alone.Despite this significant observed leakage, there remains limited awareness ofthe privacy implications of VR motion data, highlighting the pressing need forprivacy-preserving mechanisms in multi-user VR applications.

Inferring Private Personal Attributes of Virtual Reality Users from Head and Hand Motion Data