  Gait recognition is the process of identifying humans from their bipedal
locomotion such as walking or running. As such, gait data is privacy sensitive
information and should be anonymized where possible. With the rise of higher
quality gait recording techniques, such as depth cameras or motion capture
suits, an increasing amount of detailed gait data is captured and processed.
The introduction and rise of the Metaverse is an example of a potentially
popular application scenario in which the gait of users is transferred onto
digital avatars. As a first step towards developing effective anonymization
techniques for high-quality gait data, we study different aspects of movement
data to quantify their contribution to gait recognition. We first extract
categories of features from the literature on human gait perception and then
design experiments for each category to assess how much the information they
contain contributes to recognition success. We evaluated the utility of gait
perturbation by means of naturalness ratings in a user study. Our results show
that gait anonymization will be challenging, as the data is highly redundant
and inter-dependent.
