Eye-tracking technology is being increasingly integrated into mixed reality devices. Although critical applications are being enabled, there are significant possibilities for violating user privacy expectations. We show that there is an appreciable risk of unique user identification even under natural viewing conditions in virtual reality. This identification would allow an app to connect a user's personal ID with their work ID without needing their consent, for example. To mitigate such risks we propose a framework that incorporates gatekeeping via the design of the application programming interface and via software-implemented privacy mechanisms. Our results indicate that these mechanisms can reduce the rate of identification from as much as 85% to as low as 30%. The impact of introducing these mechanisms is less than 1.5$^\circ$ error in gaze position for gaze prediction. Gaze data streams can thus be made private while still allowing for gaze prediction, for example, during foveated rendering. Our approach is the first to support privacy-by-design in the flow of eye-tracking data within mixed reality use cases.
The Impact of Device Type, Data Practices, and Use Case Scenarios on Privacy Concerns about Eye-tracked Augmented Reality in the United States and Germany
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
Technical University of Munich, School of Social Sciences and Technology, Department of Educational Sciences, Chair for Human-Centered Technologies for Learning（慕尼黑技术大学，社会科学与技术学院，教育科学系，人机学习中心）
"What you think is private is no longer" -- Investigating the Aftermath of Shoulder Surfing on Smartphones in Everyday Life through the Eyes of the Victims
"These cameras are just like the Eye of Sauron": A Sociotechnical Threat Model for AI-Driven Smart Home Devices as Perceived by UK-Based Domestic Workers
King’s College London（伦敦国王学院）；University of St Andrews（圣安德鲁斯大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；INGENIO (CSIC-UPV)（INGENIO（西班牙国家研究委员会-瓦伦西亚理工大学））
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Brno University of Technology（布尔诺理工大学）；Kempelen Institute of Intelligent Technologies（肯佩伦智能技术研究所）；University of Amsterdam（阿姆斯特丹大学）；Adobe Research（Adobe研究院）；Radboud University（拉德堡德大学）
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
College of Engineering and Information Technology, University of Dubai, UAE（工程与信息技术学院，迪拜大学）；School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Australia（电气、计算机与电信工程学院，沃尔longong大学）；College of Medicine and Health Sciences, Mohammed Bin Rashid University Dubai, UAE（医学与健康科学学院，迪拜穆罕默德·本·拉希德大学）
QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Institute of Information Security, Institute of Visualisation and Interactive Systems, University of Stuttgart（信息安全部、可视化与交互系统研究所、斯图加特大学）；Institute of Information Security, University of Stuttgart（信息安全部、斯图加特大学）；Institute of Visualisation and Interactive Systems, University of Stuttgart（可视化与交互系统研究所、斯图加特大学）
