Effect of Data Degradation on Motion Re-Identification

TL;DR

This paper investigates how various types of data degradation affect the ability to identify users through motion data, revealing that current identification methods remain highly effective despite data quality reductions.

Contribution

It provides a systematic analysis of the robustness of motion-based identification against data degradation, highlighting challenges in anonymizing such data.

Findings

State-of-the-art attacks maintain near-perfect accuracy despite data degradation.

Degradation types include noise addition, framerate reduction, precision loss, and dimensionality reduction.

Results justify the difficulty of anonymizing motion data with simple degradation techniques.

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.