Abstract
A transformation of data in statistical databases is proposed to hide the presence of an individual. The transformation employs a cascade of spectral whitening and colouring (named recolouring for brevity) that preserves the first- and second-order statistical properties of the true data (i.e. mean and correlation). A measure of practical indistinguishability is introduced for the presence of the individual to be hidden (the Impact Factor), and the transformation is applied to a toy model for the case of correlated data following a Gaussian copula model. It is shown that the Impact Factor is a multiple of what would be achieved with noise addition: the proposed recolouring transformation significantly enlarges the range of attribute values for which the presence of the individual of interest cannot be reliably inferred.
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Naldi, M., Mazzoccoli, A., D’Acquisto, G. (2018). Hiding Alice in Wonderland: A Case for the Use of Signal Processing Techniques in Differential Privacy. In: Medina, M., Mitrakas, A., Rannenberg, K., Schweighofer, E., Tsouroulas, N. (eds) Privacy Technologies and Policy. APF 2018. Lecture Notes in Computer Science(), vol 11079. Springer, Cham. https://doi.org/10.1007/978-3-030-02547-2_5
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