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 entire data set is no more than a threshold t. Here, we define a privacy measure in terms of
information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by
the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.
CitationRebollo-Monedero, D.; Forné, J.; Domingo-Ferrer, J. From t-closeness-like privacy to postrandomization via information theory. "IEEE transactions on knowledge and data engineering", 08 Octubre 2009.
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