GaitGuard: Towards Private Gait in Mixed Reality
CoRR(2023)
摘要
Augmented/Mixed Reality (AR/MR) devices are unique from other mobile systems
because of their capability to offer an immersive multi-user collaborative
experience. While previous studies have explored privacy and security aspects
of multiple user interactions in AR/MR, a less-explored area is the
vulnerability of gait privacy. Gait is considered a private state because it is
a highly individualistic and a distinctive biometric trait. Thus, preserving
gait privacy in emerging AR/MR systems is crucial to safeguard individuals from
potential identity tracking and unauthorized profiling.
This paper first introduces GaitExtract, a framework designed to
automatically detect gait information in humans, shedding light on the nuances
of gait privacy in AR/MR. In this paper, we designed GaitExtract, a framework
that can automatically detect the outside gait information of a human and
investigate the vulnerability of gait privacy in AR. In a user study with $20$
participants, our findings reveal that participants were uniquely identifiable
with an accuracy of up to $78\%$ using GaitExtract. Consequently, we propose
GaitGuard, a system that safeguards gait information of people appearing in the
camera view of the AR/MR device.
Furthermore, we tested GaitGuard in an MR collaborative application,
achieving $22$ fps while streaming mitigated frames to the collaborative
server. Our user-study survey indicated that users are more comfortable with
releasing videos of them walking when GaitGuard is applied to the frames. These
results underscore the efficacy and practicality of GaitGuard in mitigating
gait privacy concerns in MR contexts.
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