Privacy-aware eye tracking using differential privacy
Privacy-aware eye tracking using differential privacy
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DOI：
10.1145/3314111.3319915
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Julian Steil;Inken Hagestedt;Michael Xuelin Huang;A. Bulling
中科院分区：
文献类型：
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作者：
Julian Steil;Inken Hagestedt;Michael Xuelin Huang;A. Bulling
随着眼球跟踪越来越多地被整合到虚拟和增强现实(VR/AR)头盔显示器中，保护用户的隐私在眼球跟踪界是一个越来越重要但却没有得到充分探索的话题。我们报告了一项关于眼球跟踪隐私方面的大规模在线调查(N=124)，它提供了第一个全面的帐户，与谁，为哪些服务，以及在多大程度上用户愿意分享他们的凝视数据。基于这些见解，我们设计了一个隐私感知的VR界面，它使用了区分隐私，并在一个新的20个参与者的数据集上对两个隐私敏感任务进行了评估：我们的方法可以防止用户重新识别和保护性别信息，同时保持了基于凝视的文档类型分类的高性能。我们的结果突出了凝视数据时的隐私挑战，并表明差异隐私是解决这些挑战的一种潜在手段。因此，本文为未来隐私感知凝视界面的研究奠定了重要的基础。
With eye tracking being increasingly integrated into virtual and augmented reality (VR/AR) head-mounted displays, preserving users' privacy is an ever more important, yet under-explored, topic in the eye tracking community. We report a large-scale online survey (N=124) on privacy aspects of eye tracking that provides the first comprehensive account of with whom, for which services, and to what extent users are willing to share their gaze data. Using these insights, we design a privacy-aware VR interface that uses differential privacy, which we evaluate on a new 20-participant dataset for two privacy sensitive tasks: We show that our method can prevent user re-identification and protect gender information while maintaining high performance for gaze-based document type classification. Our results highlight the privacy challenges particular to gaze data and demonstrate that differential privacy is a potential means to address them. Thus, this paper lays important foundations for future research on privacy-aware gaze interfaces.