Virtual reality (VR) platforms and apps collect user sensor data, including motion, facial, eye, and hand data, in abstracted form. These data may expose users to unique privacy risks without their knowledge or meaningful awareness, yet the extent of these risks remains understudied. To address this gap, we propose VR ProfiLens, a framework to study user profiling based on VR sensor data and the resulting privacy risks across consumer VR apps. To systematically study this problem, we first develop a taxonomy rooted in the CCPA definition of personal information and expand it by sensor, app, and threat contexts to identify user attributes at risk. Then, we conduct a user study in which we collect VR sensor data from four sensor groups from real users interacting with 10 popular consumer VR apps, followed by a survey. We design and apply an analysis pipeline to demonstrate the feasibility of inferring user attributes using these data. Our results show that sensitive personal information can be inferred with moderately high to high risk (up to 90% F1 score) from abstracted sensor data. Through feature analysis, we further identify correlations among app groups and sensor groups in inferring user attributes. Our findings highlight risks to users, including privacy loss, tracking, targeted advertising, and safety threats. Finally, we discuss design implications and regulatory recommendations to enhance transparency and better protect users' privacy in VR.

翻译：虚拟现实（VR）平台与应用以抽象形式收集用户传感器数据，包括动作、面部、眼部及手部数据。这些数据可能在用户不知情或缺乏实质性认知的情况下，使其面临独特的隐私风险，然而此类风险的程度尚未得到充分研究。为填补这一空白，我们提出VR ProfiLens框架，用于基于VR传感器数据研究用户画像及其在消费级VR应用中引发的隐私风险。为系统性地研究该问题，我们首先以《加州消费者隐私法案》（CCPA）对个人信息的定义为根基构建分类体系，并依据传感器、应用及威胁情境进行扩展，以识别处于风险中的用户属性。随后，我们开展了一项用户研究，从真实用户与10款热门消费级VR应用的交互过程中收集四类传感器组的VR数据，并辅以问卷调查。我们设计并应用了一套分析流程，以论证利用这些数据推断用户属性的可行性。研究结果表明，从抽象传感器数据中可推断出敏感个人信息，风险等级为中度偏高至高（F1分数最高达90%）。通过特征分析，我们进一步揭示了不同应用组与传感器组在推断用户属性时的关联性。我们的发现凸显了用户面临的多种风险，包括隐私泄露、行为追踪、定向广告及安全威胁。最后，我们探讨了设计层面的启示与监管建议，以提升透明度并更好地保护VR用户的隐私。