Issue No. 11 - November (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.190
David Rebollo-Monedero , Technical University of Catalonia, Barcelona
Jordi Forné , Technical University of Catalonia, Barcelona
Josep Domingo-Ferrer , Rovira i Virgili University, Tarragona
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.
t-Closeness, microdata anonymization, information theory, rate-distortion theory, PRAM, noise addition.
J. Domingo-Ferrer, J. Forné and D. Rebollo-Monedero, "From t-Closeness-Like Privacy to Postrandomization via Information Theory," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1623-1636, 2009.