Analysis of iris obfuscation: Generalising eye information processes for privacy studies in eye tracking.

We present a framework to model and evaluate obfuscation methods for removing sensitive information in eye-tracking. The focus is on preventing iris-pattern identification. Candidate methods have to be effective at removing information while retaining high utility for gaze estimation. We propose several obfuscation methods that drastically outperform existing ones. A stochastic grid-search is used to determine optimal method parameters and evaluate the model framework. Precise obfuscation and gaze effects are measured for selected parameters. Two attack scenarios are considered and evaluated. We show that large datasets are susceptible to probabilistic attacks, even with seemingly effective obfuscation methods. However, additional data is needed to more accurately access the probabilistic security.