GaitGuard: 保护混合现实中的基于视频的步态隐私
GaitGuard: Protecting Video-Based Gait Privacy in Mixed Reality
AI总结：
GaitGuard通过实时处理混合现实视频流，有效降低基于视频的步态分析准确率，同时保持视觉质量，提供隐私保护解决方案。
摘要：
混合现实（MR）系统捕获连续视频流，这些视频流暴露了旁观者和合作者的步态模式——一种揭示敏感属性的生物特征，包括年龄、性别和健康状况。我们证明，未受保护的MR流中基于视频的步态分析准确率达78%（15.6倍随机猜测），这促使我们提出GaitGuard，一种在配套移动设备上实时运行的防御系统。GaitGuard引入了GaitExtract，一种从临床分析中改编而来的自动化步态特征提取流程，适用于以自身为中心的MR视角。通过系统评估233种缓解配置，我们characterized隐私-效用-性能的权衡。一个关键见解是步态特征主要来自短暂事件（脚跟触地、脚尖离地）。我们利用这种时间稀疏性，通过自适应缓解方法，只处理步态关键帧，从而在保持视觉质量（SSIM: 0.97）的同时，将分析准确率降低了68%（在29 FPS下）。GaitGuard能够扩展到10个同时用户，延迟低于10毫秒。对20名参与者的定性研究证实，用户更倾向于使用GaitGuard这样的解决方案，因为它提供了隐私保障。
英文摘要：
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$\times$ random chance) on unprotected MR feeds, motivating \textbf{GaitGuard}, a real-time defense operating on a companion mobile device. GaitGuard introduces \textbf{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. \textbf{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 \textbf{GaitGuard} which provides privacy guarantees.
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