Abstract
t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the data is no more than a threshold t. We state here the t-closeness property in terms of information theory and then use the tools of that theory to show that t-closeness can be achieved by the PRAM masking method in the discrete case and by a form of noise addition in the general case.
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Rebollo-Monedero, D., Forné, J., Domingo-Ferrer, J. (2008). From t-Closeness to PRAM and Noise Addition Via Information Theory. In: Domingo-Ferrer, J., Saygın, Y. (eds) Privacy in Statistical Databases. PSD 2008. Lecture Notes in Computer Science, vol 5262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87471-3_9
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DOI: https://doi.org/10.1007/978-3-540-87471-3_9
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