Measuring risk and utility of anonymized data using information theory
Proceedings of the 2009 EDBT/ICDT Workshops
Private location-based information retrieval through user collaboration
Computer Communications
Optimized query forgery for private information retrieval
IEEE Transactions on Information Theory
Identity in the Information Society
An information theoretic privacy and utility measure for data sanitization mechanisms
Proceedings of the second ACM conference on Data and Application Security and Privacy
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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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.