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t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

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3 Author(s)
Ninghui Li ; Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA ; Tiancheng Li ; Venkatasubramanian, S.

The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We choose to use the earth mover distance measure for our t-closeness requirement. We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments.

Published in:

Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on

Date of Conference:

15-20 April 2007