2023/12/07 by Romero, Diana, Markopoulou, Athina, Elmalaki, Salma
#Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC)
paper · doi:10.48550/arxiv.2312.04470
Mixed Reality (MR) systems capture continuous video streams that expose bystanders' and collaborators' gait patterns -- a biometric revealing sensitive attributes including age, gender, and health conditions. We show that video-based gait profiling achieves 78% accuracy (15.6× random chance) on unprotected MR feeds, motivating GaitGuard, a real-time defense operating on a companion mobile device. GaitGuard introduces GaitExtract, an automated gait feature extraction pipeline adapted from clinical analysis for egocentric MR perspectives. Through systematic evaluation of 233 mitigation configurations, we characterize privacy-utility-performance trade-offs. A key insight is that gait features derive primarily from transient events (heel strikes, toe-offs). We exploit this temporal sparsity through adaptive mitigation that selectively processes only gait-critical frames, achieving a 68% reduction in profiling accuracy while preserving visual quality (SSIM: 0.97) at 29~FPS. GaitGuard scales to 10 simultaneous users with under 10ms latency. A qualitative study of 20-participants confirms that the users preferred a solution such as GaitGuard which provides privacy guarantees.