The contents of this legacy page are no longer maintained nor supported, and are made available only for historical purposes.
Bibliography Details
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. |