UC Irvine

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 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 adopts and automates a framework designed to detect gait information in humans, referred to in this work as GaitExtract.GaitExtract can automatically detect the neighbor 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 real-time system that safeguards the gait information of people appearing in the camera view of the AR/MR device (a.k.a. bystanders). We tested GaitGuard in an MR collaborative application, achieving 22 fps while streaming mitigated frames to the collaborative server. Furthermore, our qualitative surveys indicated that users are more comfortable with releasing videos of them walking when GaitGuard is applied to the camera frames. These results underscore the efficacy and practicality of GaitGuard in mitigating gait privacy concerns in MR contexts.