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Flexible Anonymization For Privacy Preserving Data Publishing: A Systematic Search Based Approach

9 years 1 months ago
Flexible Anonymization For Privacy Preserving Data Publishing: A Systematic Search Based Approach
k-anonymity is a popular measure of privacy for data publishing: It measures the risk of identity-disclosure of individuals whose personal information are released in the form of published data for statistical analysis and data mining purposes(e.g. census data). Higher values of k denote higher level of privacy (smaller risk of disclosure). Existing techniques to achieve k-anonymity use a variety of “generalization” and “suppression” of cell values for multi-attribute data. At the same time, the released data needs to be as “information-rich” as possible to maximize its utility. Information loss becomes an even greater concern as more stringent privacy constraints are imposed [4]. The resulting optimization problems have proven to be computationally intensive for data sets with large attribute-domains. In this paper, we develop a systematic enumeration based branchand-bound technique that explores a much richer space of solutions than any previous method in literature. We ...
Bijit Hore, Ravi Chandra Jammalamadaka, Sharad Meh
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where SDM
Authors Bijit Hore, Ravi Chandra Jammalamadaka, Sharad Mehrotra
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