For Your Eyes Only: Privacy-preserving eye-Tracking datasets

Abstract

Eye-Tracking is a critical source of information for understanding human behavior and developing future mixed-reality technology. Eye-Tracking enables applications that classify user activity or predict user intent. However, eye-Tracking datasets collected during common virtual reality tasks have also been shown to enable unique user identification, which creates a privacy risk. In this paper, we focus on the problem of user re-identification from eye-Tracking features. We adapt standardized privacy definitions of k-Anonymity and plausible deniability to protect datasets of eye-Tracking features, and evaluate performance against re-identification by a standard biometric identification model on seven VR datasets. Our results demonstrate that re-identification goes down to chance levels for the privatized datasets, even as utility is preserved to levels higher than 72% accuracy in document type classification.