发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探索在XR系统中，通过选择不同注视表征（原始注视、空间注意力热图、工程化眼动特征）作为轻量隐私控制，在保留约85%动作识别准确率的同时，将206个身份的重识别风险降低约一个数量级，为隐私设计提供透明补充手段。
AI 中文摘要
智能扩展现实（XR）系统日益利用眼动与头部跟踪来推断用户的意图、任务和注意力，但这些信号同时也可能泄露生物识别身份。我们研究注视数据表征的选择是否可作为特征提取阶段的轻量隐私控制手段，且该手段需在添加扰动或正式隐私机制之前发挥作用。我们使用以自我为中心的HoloAssist数据集，在模型容量匹配的前提下，对比三种注视表征：原始注视、空间注意力热图以及经工程设计的眼动特征。我们针对两种任务对每种表征进行评估：一是作为任务效用的动作识别，二是作为隐私泄露的闭集用户重识别。表征选择会显著改变隐私-效用权衡：经工程设计的特征保留了原始注视约85%的动作识别准确率，同时将重识别风险降低约一个数量级，在206个身份上的重识别率约为偶然猜测率的4倍。这种降低仅能减弱而非消除身份泄露，且不同表征间的差异表明，仅靠抽象无法保证隐私。经工程设计的特征呈现出可解释且可审计的结构，为设计者提供了一种透明的隐私控制手段，可作为差分隐私等机制的补充。
英文摘要
Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, and attention, but the same signals can also reveal biometric identity. We study whether gaze data representation choice can serve as a lightweight privacy control at feature extraction, before adding perturbation or formal privacy mechanisms. Using the egocentric HoloAssist dataset, we compare three gaze representations under matched model capacity: raw gaze, spatial attention heatmaps, and engineered eye-movement features. We evaluate each representation on action recognition as task utility and closed-set user re-identification as privacy leakage. Representation choice substantially changes the privacy-utility tradeoff. Engineered features retain roughly 85% of raw gaze's action-recognition accuracy while reducing re-identification by about an order of magnitude, to roughly four times the chance rate across 206 identities. This reduction attenuates rather than eliminates identity leakage, and the differences across representations show that abstraction alone does not guarantee privacy. Engineered features expose interpretable and auditable structure, giving designers a transparent privacy lever that complements mechanisms such as differential privacy.
