VR ProfiLens: User Profiling Risks in Consumer Virtual Reality Apps
Ismat Jarin, Olivia Figueira, Yu Duan, Tu Le, Athina Markopoulou

TL;DR
This paper introduces VR ProfiLens, a framework that demonstrates how VR sensor data can infer sensitive user attributes, revealing significant privacy risks and informing better privacy protections in consumer VR applications.
Contribution
The paper presents a systematic framework and empirical analysis showing high accuracy in inferring personal attributes from VR sensor data, highlighting privacy risks in consumer VR apps.
Findings
Sensitive personal info can be inferred with up to 90% accuracy.
Correlations exist between app groups, sensor data, and user attributes.
VR sensor data poses significant privacy and safety risks.
Abstract
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…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · User Authentication and Security Systems · Advanced Malware Detection Techniques
