Before revision:  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 .
After revision:  Significant reductions in correlations in addition to query sensitivities are illustrated , 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 .