1. Effect of Duration and Delay on the Identifiability of VR Motion
- Author
-
Miller, Mark Roman, Nair, Vivek, Han, Eugy, DeVeaux, Cyan, Rack, Christian, Wang, Rui, Huang, Brandon, Latoschik, Marc Erich, O'Brien, James F., and Bailenson, Jeremy N.
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Human-Computer Interaction - Abstract
Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments., Comment: 6 pages, 2 figures, presented at the SePAR workshop (Security and Privacy in Mixed, Augmented, and Virtual Realities), co-located with WoWMoM 2024. arXiv admin note: text overlap with arXiv:2303.01430
- Published
- 2024
- Full Text
- View/download PDF