Vetting Privacy Policies in Virtual Reality Platforms With Longitudinal Analysis

With the help of advanced sensors, virtual reality (VR) apps provide users with an immersive experience, but they also have the potential to collect a wider range of user data compared to traditional web and mobile apps. As a result, increasing numbers of regulations are being introduced globally, emphasizing the need for app developers to provide privacy policies that inform users about data collection, usage, and sharing (CUS) process. Unfortunately, despite the significant efforts made by VR developers to improve app performance, it remains unclear how they ensure their privacy policies comply with regulations and meet user expectations. In this study, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VPVet</small> to automatically vet privacy policy issues for VR apps. We first summarize five vetting criteria based on a study of privacy policies from popular apps: availability, completeness, granularity, minimization, and consistency. We then dissect VR data and entity ontologies and manually generate VR-related CUS sentences to fine-tune privacy policy language models, overcoming performance degradation when handling VR domain-specific sentences. Finally, we construct the largest VR privacy policy dataset to date, named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VRPP</small>, consisting of privacy policies from 11,923 VR apps across 10 mainstream platforms. These policies were crawled in late 2022 and early 2025 to investigate the evolution of the VR ecosystem. Our vetting process examines platform, app category, and longitudinal perspectives, revealing that VR privacy policies have shown severe privacy issues over the past few years, including limited availability, poor quality, coarse granularity, a lack of adaptation to VR-specific traits, and inconsistencies between CUS statements and actual app behaviors.