  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.
<meaning-changed>   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.
 Fourier Perturbation Algorithm, which is a differential privacy mechanism, is extended and a scaling mistake in its proof . Furthermore, we illustrate significant reductions in sample 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.