  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.
