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ICDE
2007
IEEE

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

15 years 3 months ago
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
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 Ea...
Ninghui Li, Tiancheng Li, Suresh Venkatasubramania
Added 20 Dec 2008
Updated 21 Feb 2012
Type Conference
Year 2007
Where ICDE
Authors Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian
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