用噪声自编码器保护眼动数据隐私，既防识别又保可用性。
Privacy Enhancement for Gaze Data Using a Noise-Infused Autoencoder
- 在潜空间注入噪声，实现隐私保护下的眼动数据重构。
- 生物特征识别准确率大幅下降，但眼动预测性能损失极小。
- 适合需保护用户隐私的互动系统与医疗研究场景。
我们提出一种基于潜空间噪声的自编码器机制，用于保护眼动信号隐私，防止用户在不同使用会话间被非授权识别，同时保持数据对良性任务的可用性。通过在生物特征识别与眼动预测任务中评估隐私-效用权衡，结果表明该方法显著降低生物特征可识别性，且对数据效用影响极小。与已有方法相比，本框架保留了生理上合理的注视模式，更适用于下游应用。该工作为眼动系统中的隐私保护提供了有效且可用的解决方案。
原文摘要 · Abstract (English)
We present a privacy-enhancing mechanism for gaze signals using a latent-noise autoencoder that prevents users from being re-identified across play sessions without their consent, while retaining the usability of the data for benign tasks. We evaluate privacy-utility trade-offs across biometric identification and gaze prediction tasks, showing that our approach significantly reduces biometric identifiability with minimal utility degradation. Unlike prior methods in this direction, our framework retains physiologically plausible gaze patterns suitable for downstream use, which produces favorable privacy-utility trade-off. This work advances privacy in gaze-based systems by providing a usable and effective mechanism for protecting sensitive gaze data.
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