  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.
