From T-Closeness-Like Privacy to Postrandomization via Information Theory

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Security
Format: PDF
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, the authors define a privacy measure in terms of information theory, similar to t-closeness. Then, they use the tools of that theory to show that their privacy measure can be achieved by the Post-RAndomization Method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.
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