Differential Privacy for Eye Tracking with Temporal Correlations

• URL: http://arxiv.org/abs/2002.08972v3
• Date: Mon, 20 Dec 2021 09:18:03 GMT
• Title: Differential Privacy for Eye Tracking with Temporal Correlations
• Authors: Efe Bozkir and Onur G\"unl\"u and Wolfgang Fuhl and Rafael F. Schaefer and Enkelejda Kasneci
• Abstract summary: New generation head-mounted displays, such as VR and AR glasses, are coming into the market with already integrated eye tracking. Since eye movement properties contain biometric information, privacy concerns have to be handled properly. We propose a novel transform-coding based differential privacy mechanism to further adapt it to the statistics of eye movement feature data.
• Score: 30.44437258959343
• License: http://creativecommons.org/licenses/by/4.0/
• 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 numerous 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 eye movement data obtained from such displays. Standard differential privacy mechanisms; however, are vulnerable due to temporal correlations between the eye movement observations. 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 and compare various low-complexity methods. We extend the 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 provide significantly high privacy without any essential loss in classification accuracies while hiding personal identifiers.

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