The recent success of deep learning (DL) has enabled the generation of high-quality synthetic gaze data. However, such data also raises privacy concerns because gaze sequences can encode subjects' internal states, like fatigue, emotional load, or stress. Ideally, synthetic gaze should preserve the signal quality of real recordings and remove or attenuate state-related, privacy-sensitive attributes. Many recent DL-based generative models focus on replicating real gaze trajectories and do not explicitly consider subjective reports or the privatization of internal states. However, in this work, we consider a recent diffusion-based gaze synthesis approach and examine correlations between synthetic gaze features and subjective reports (e.g., fatigue and related self-reported states). Our result shows that these correlations are trivial, which suggests the generative approach suppresses state-related features. Moreover, synthetic gaze preserves necessary signal characteristics similar to those of real data, which supports its use for privacy-preserving gaze-based applications.

翻译：深度学习（DL）的最新进展使得生成高质量合成凝视数据成为可能。然而，此类数据也引发了隐私担忧，因为凝视序列可能编码受试者的内部状态，如疲劳、情绪负荷或压力。理想情况下，合成凝视应保留真实记录信号的品质，同时移除或减弱与状态相关的隐私敏感属性。许多近期基于DL的生成模型专注于复现真实凝视轨迹，并未明确考虑主观报告或内部状态的私有化。然而，在本研究中，我们探讨了一种基于扩散的凝视生成方法，并检验了合成凝视特征与主观报告（如疲劳及相关自报告状态）之间的相关性。结果表明这些相关性微不足道，这暗示该生成方法抑制了状态相关特征。此外，合成凝视保留了与真实数据相似的必要信号特性，这支持了其在隐私保护凝视应用中的使用。