# A Large-Scale Study of Personal Identifiability of Virtual Reality Motion Over Time

arXiv: https://arxiv.org/abs/2303.01430 · HTML: https://mlsanity.com/paper/2303.01430/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Evaluating Deep Networks for Detecting User Familiarity with VR from Hand Interactions](https://mlsanity.com/paper/2401.16443/) (2024)
- [The Full-scale Assembly Simulation Testbed (FAST) Dataset](https://mlsanity.com/paper/2403.08969/) (2024)
- [HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models](https://mlsanity.com/paper/2604.00592/) (2026)

## Abstract (arXiv)

In recent years, social virtual reality (VR), sometimes described as the "metaverse," has become widely available. With its potential comes risks, including risks to privacy. To understand these risks, we study the identifiability of participants' motion in VR in a dataset of 232 VR users with eight weekly sessions of about thirty minutes each, totaling 764 hours of social interaction. The sample is unique as we are able to study the effect of user, session, and time independently. We find that the number of sessions recorded greatly increases identifiability, and duration per session increases identifiability as well, but to a lesser degree. We also find that greater delay between training and testing sessions reduces identifiability. Ultimately, understanding the identifiability of VR activities will help designers, security professionals, and consumer advocates make VR safer.