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N. Li, T. Li, and S. Venkatasubramanian, "t-Closeness: Privacy Beyond k-Anonymity and l-Diversity", in IEEE International Conference on Data Engineering (ICDE), 2007.
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Authors: N. Li
T. Li
S. Venkatasubramanian
Published: IEEE International Conference on Data Engineering (ICDE), 2007
URL:http://www.cs.purdue.edu/homes/ninghui/papers/t_closeness_icde07.pdf
ENTRY DATE: 2008-06-16
ABSTRACT: 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.
  Last Modified: Fri Oct-25-2019 12:31:28 PDT
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