选对眼动数据表示法，能有效降低隐私泄露风险同时保持功能可用性。
Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR
- 用工程化眼动特征替代原始眼动数据，实现轻量级隐私控制
- 保留原始数据85%动作识别准确率，身份重识别率下降近十倍
- 特征可解释性强，适合需要透明隐私设计的系统开发者
智能扩展现实（XR）系统依赖眼动与头部追踪推断用户意图，但这些信号也可能暴露生物特征身份。本文在同模型容量下比较三种眼动表示：原始眼动、空间注意力热图和工程化眼动特征。以动作识别为任务效用，闭集身份重识别为隐私泄露指标。结果表明，工程化特征在保持约85%原始眼动动作识别准确率的同时，将206个身份的重识别率降至接近四倍随机猜测水平，降幅约一个数量级。该方法虽不能彻底消除泄露，但揭示了抽象本身不足以保障隐私。其可解释结构为设计师提供了可审计的隐私调控手段，可与差分隐私等机制协同使用。
原文摘要 · Abstract (English)
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.
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