A Multi-Store Privacy Measurement of Virtual Reality App Ecosystem
Chuan Yan, Zeng Li, Kunlin Cai, Liuhuo Wan, Ruomai Ren, Yiran Shen, Guangdong Bai

TL;DR
This study provides a comprehensive analysis of privacy practices across major VR app stores, revealing widespread privacy compliance issues and highlighting the need for stricter regulations to protect user data in the VR ecosystem.
Contribution
It is the first large-scale, multi-store analysis of privacy practices in the VR app ecosystem, combining NLP, reverse engineering, and static analysis techniques.
Findings
One third of VR apps do not declare sensitive data use.
21.5% of apps lack valid privacy policies.
Significant privacy compliance issues are prevalent across all stores.
Abstract
Virtual Reality (VR) has gained increasing traction among various domains in recent years, with major companies such as Meta, Pico, and Microsoft launching their application stores to support third-party developers in releasing their applications (or simply apps). These apps offer rich functionality but inherently collect privacy-sensitive data, such as user biometrics, behaviors, and the surrounding environment. Nevertheless, there is still a lack of domain-specific regulations to govern the data handling of VR apps, resulting in significant variations in their privacy practices among app stores. In this work, we present the first comprehensive multi-store study of privacy practices in the current VR app ecosystem, covering a large-scale dataset involving 6,565 apps collected from five major app stores. We assess both declarative and behavioral privacy practices of VR apps, using a…
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