Computer Graphics

University of California - Berkeley

Effect of Data Degradation on Motion Re-Identification

Abstract

The use of virtual and augmented reality devices is increasing, but these sensor-rich devices pose risks to privacy. The ability to track a user’s motion and infer the identity or characteristics of the user poses a privacy risk that has received significant attention. Existing deep-network-based defenses against this risk, however, require significant amounts of training data and have not yet been shown to generalize beyond specific applications. In this work, we study the effect of signal degradation on identifiability, specifically through added noise, reduced framerate, reduced precision, and reduced dimensionality of the data. Our experiment shows that state-of-the-art identification attacks still achieve near-perfect accuracy for each of these degradations. This negative result demonstrates the difficulty of anonymizing this motion data and gives some justification to the existing data- and compute-intensive deep-network based methods.

Citation

Vivek Nair, Mark Roman Miller, Rui Wang, Brandon Huang, Christian Rack, Marc Erich Latoschik, and James F. O'Brien. "Effect of Data Degradation on Motion Re-Identification". In 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pages 85–90. IEEE Computer Society, June 2024. Presented at International Workshop on Privacy and Security in Augmented, Virtual, and eXtended Realities.