VPVet: Vetting Privacy Policies of Virtual Reality Apps
Yuxia Zhan, Yan Meng, Lu Zhou, Yichang Xiong, Xiaokuan Zhang, Lichuan, Ma, Guoxing Chen, Qingqi Pei, Haojin Zhu

TL;DR
VPVet is an automated tool designed to evaluate the compliance and quality of privacy policies in VR apps, revealing significant privacy issues and inconsistencies in the current VR ecosystem.
Contribution
This paper introduces VPVet, the first system to analyze VR privacy policies for compliance, completeness, and quality, supported by the largest VR privacy policy dataset to date.
Findings
Many VR privacy policies are incomplete or poorly written.
There are significant inconsistencies between policies and actual app behaviors.
VR privacy policies lack granularity and adaptation to VR-specific data collection.
Abstract
Virtual reality (VR) apps can harvest a wider range of user data than web/mobile apps running on personal computers or smartphones. Existing law and privacy regulations emphasize that VR developers should inform users of what data are collected/used/shared (CUS) through privacy policies. However, privacy policies in the VR ecosystem are still in their early stages, and many developers fail to write appropriate privacy policies that comply with regulations and meet user expectations. In this paper, we propose VPVet to automatically vet privacy policy compliance issues for VR apps. VPVet first analyzes the availability and completeness of a VR privacy policy and then refines its analysis based on three key criteria: granularity, minimization, and consistency of CUS statements. Our study establishes the first and currently largest VR privacy policy dataset named VRPP, consisting of privacy…
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Taxonomy
TopicsPrivacy, Security, and Data Protection
