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Issue No.11 - November (2010 vol.22)

pp: 1623-1636

Jordi Forné , Technical University of Catalonia, Barcelona

David Rebollo-Monedero , Technical University of Catalonia, Barcelona

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.190

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 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.

INDEX TERMS

t-Closeness, microdata anonymization, information theory, rate-distortion theory, PRAM, noise addition.

CITATION

Jordi Forné, David Rebollo-Monedero, "From t-Closeness-Like Privacy to Postrandomization via Information Theory",

*IEEE Transactions on Knowledge & Data Engineering*, vol.22, no. 11, pp. 1623-1636, November 2010, doi:10.1109/TKDE.2009.190REFERENCES

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