Effect of Duration and Delay on the Identifiability of VR Motion

PubDate: Jul 2024

Teams::Illinois Institute of Technology Chicago；UC Berkeley；Stanford University；University of Wurzburg；Carnegie Mellon Universit

Writers: Mark Roman Miller, Vivek Nair, Eugy Han, Cyan DeVeaux, Christian Rack, Rui Wang, Brandon Huang, Marc Erich Latoschik, James F. O'Brien, Jeremy N. Bailenson

PDF:Effect of Duration and Delay on the Identifiability of VR Motion

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.