Abstract
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.
Highlights
Recent advances in the field of head-mounted displays (HMDs), computer graphics, and eye tracking enable easy access to pervasive eye trackers along with modern HMDs
Instead of only using Support Vector Machines (SVM) as in previous works [20, 29], we evaluate a set of classifiers including SVMs, decision trees (DTs), random forests (RFs), and k-Nearest Neighbors
As previous results quickly drop to the 0.33 guessing probability in high privacy regions, we significantly outperform them with Difference- and chunk-based FPA (DCFPA) and Fourier Perturbation Algorithm (FPA) with the accuracies over 0.60 and 0.85, respectively
Summary
Recent advances in the field of head-mounted displays (HMDs), computer graphics, and eye tracking enable easy access to pervasive eye trackers along with modern HMDs. Soon, the usage of such devices might result in a significant increase in the amount of eye movement data collected from users across different application domains such as gaming, entertainment, or education. In virtual and augmented reality (VR/AR), it is possible to derive plenty of sensitive information about users from the eye movement data. It has been shown that eye tracking signals can be employed for activity recognition even in challenging everyday tasks [1,2,3], to detect cognitive load [4, 5], mental fatigue [6], and many other user states. Assessment of situational attention [7], expert-novice analysis in areas
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.