Paper 2020/340

Differential Privacy for Eye Tracking with Temporal Correlations

Efe Bozkir and Onur Gunlu and Wolfgang Fuhl and Rafael F. Schaefer and Enkelejda Kasneci

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 many 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 the eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable to temporal correlations in the eye movement features. 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 by comparing various low-complexity methods. We extent 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 show significantly high privacy without loss in classification accuracies as well.

Note: Authors Efe Bozkir and Onur Gunlu contributed equally.