  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.
