Head mounted displaysbring 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 .
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 .
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Head mounted displaysbring eye tracking into daily use and this raises privacy concerns for users
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
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