# SoK: Data Privacy in Virtual Reality

arXiv: https://arxiv.org/abs/2301.05940 · HTML: https://mlsanity.com/paper/2301.05940/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2023.

## Built on by (most cited first, 6 of 6)

- [Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data](https://mlsanity.com/paper/2302.08927/) (2023)
- [Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges](https://mlsanity.com/paper/2305.14080/) (2023)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [How LLMs are Shaping the Future of Virtual Reality](https://mlsanity.com/paper/2508.00737/) (2025)
- [Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks](https://mlsanity.com/paper/2501.18727/) (2025)
- [Deep Learning for Virtual Reality User Identification: A Benchmark](https://mlsanity.com/paper/2604.16341/) (2026)

## Abstract (arXiv)

The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position the so-called "metaverse" as the next major medium for accessing and interacting with the internet. While consumers have become accustomed to a degree of data harvesting on the web, the real-time nature of data sharing in the metaverse indicates that privacy concerns are likely to be even more prevalent in the new "Web 3.0." Research into VR privacy has demonstrated that a plethora of sensitive personal information is observable by various would-be adversaries from just a few minutes of telemetry data. On the other hand, we have yet to see VR parallels for many privacy-preserving tools aimed at mitigating threats on conventional platforms. This paper aims to systematize knowledge on the landscape of VR privacy threats and countermeasures by proposing a comprehensive taxonomy of data attributes, protections, and adversaries based on the study of 68 collected publications. We complement our qualitative discussion with a statistical analysis of the risk associated with various data sources inherent to VR in consideration of the known attacks and defenses. By focusing on highlighting the clear outstanding opportunities, we hope to motivate and guide further research into this increasingly important field.