Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

Abstract

Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors. One of the challenges with regard to the social acceptance of eye tracking technology is however the preserving of sensitive and personal information. To tackle this challenge, we employ a privacy-preserving framework based on randomized encoding to train a Support Vector Regression model using synthetic eye images privately to estimate the human gaze. During the computation, none of the parties learn about the data or the result that any other party has. Furthermore, the party that trains the model cannot reconstruct pupil, blinks or visual scanpath. The experimental results show that our privacy-preserving framework is capable of working in real-time, with the same accuracy as compared to non-private version and could be extended to other eye tracking related problems.

In Symposium on Eye Tracking Research and Applications (ETRA '20). Authors' copy of the published paper, refer to the doi for the definitive version

References in corpus (4)

• Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
• Privacy-Aware Eye Tracking Using Differential Privacy
• PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features
• Differential Privacy for Eye-Tracking Data

Cited by in corpus (6)

• A privacy-preserving approach to streaming eye-tracking data
• Differential Privacy for Eye Tracking with Temporal Correlations
• Privacy-Preserving Eye Videos using Rubber Sheet Model
• Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking
• Eye Tracking Data Collection Protocol for VR for Remotely Located Subjects using Blockchain and Smart Contracts
• 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