New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking and are expected to enable novel ways of human-computer interaction in numerous applications. However, since eye movement properties contain biometric information, privacy concerns have to be handled properly. Privacy-preservation techniques such as differential privacy mechanisms have recently been applied to eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. In this work, we propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data and compare various low-complexity methods. We extend the Fourier perturbation algorithm, which is a differential privacy mechanism, and correct a scaling mistake in its proof. Furthermore, we illustrate significant reductions in sample correlations in addition to query sensitivities, which provide the best utility-privacy trade-off in the eye tracking literature. Our results provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.
翻译:新一代的头顶显示器,如VR和AR眼镜,正在进入市场,已经集成的眼睛跟踪,预计能够在许多应用中采用新的人类-计算机互动方式。然而,由于眼运动特性包含生物鉴别信息,隐私问题必须得到适当处理。隐私保护技术,例如不同的隐私机制,最近已应用于从这些显示器获得的眼运动数据。标准差异隐私机制;但由于眼运动观测之间的时间相关性,很容易出现。在这项工作中,我们提议建立一个新型的变换编码差异隐私机制,以进一步使其适应眼运动特征数据的统计数据,比较各种低复杂性方法。我们推广了四维的扰动算法,这是一种差异隐私机制,并纠正其证据中的缩放错误。此外,我们指出,除了查询敏感度之外,样本相关性显著下降,这为眼睛跟踪文献提供了最佳的实用-隐私交易。我们的成果提供了大量高隐私,在分类时不会造成任何基本损失,同时隐藏个人识别特征。