Motion tracking "telemetry" data lies at the core of nearly all modern virtual reality (VR) and metaverse experiences. While generally presumed innocuous, recent studies have demonstrated that motion data actually has the potential 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 by analyzing motion data recorded by VR devices. We conducted a large-scale survey of VR users (N=1,006) with dozens of questions ranging from background and demographics to behavioral patterns and health information. We then collected VR motion samples of each user playing the game ``Beat Saber,'' and attempted to infer their survey responses using just their head and hand motion patterns. Using simple machine learning models, many of these attributes could accurately and consistently be inferred from VR motion data alone, highlighting the pressing need for privacy-preserving mechanisms in multi-user VR applications.

翻译：运动追踪数据（即"遥测"数据）几乎构成所有现代虚拟现实（VR）与元宇宙体验的核心。尽管通常被认为无害，但近期研究表明运动数据实际上具有唯一识别VR用户的潜力。本研究更进一步表明，仅通过分析VR设备记录的运动数据即可推断出多种用户隐私信息。我们对1,006名VR用户开展了大规模调查，涵盖背景与人口统计信息、行为模式及健康信息等数十个问题。随后收集了每位用户游玩《Beat Saber》游戏时的VR运动样本，并尝试仅通过其头部与手部运动模式推断问卷调查结果。使用简单机器学习模型，这些属性中有相当一部分能够基于VR运动数据实现准确且稳定的推断，这凸显了多用户VR应用中隐私保护机制的迫切需求。