Differential Privacy for Eye Tracking with Temporal Correlations

Head mounted displays bring eye tracking into daily use and this raises privacy concerns for users. Privacy-preservation techniques such as differential privacy mechanisms are recently applied to the eye tracking data obtained from such displays; however, standard differential privacy mechanisms are vulnerable to temporal correlations in the eye movement features. In this work, a transform coding based differential privacy mechanism is proposed for the first time in the eye tracking literature to further adapt it to statistics of eye movement feature data by comparing various low-complexity methods. Fourier Perturbation Algorithm, which is a differential privacy mechanism, is extended and a scaling mistake in its proof is corrected. Significant reductions in correlations in addition to query sensitivities are illustrated, which provide the best utility-privacy trade-off in the literature for the eye tracking dataset used. The differentially private eye movement data are evaluated also for classification accuracies for gender and document-type predictions to show that higher privacy is obtained without a reduction in the classification accuracies by using proposed methods.