Generating Microdata with P -Sensitive K -Anonymity Property

Existing privacy regulations together with large amounts of available data have created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α, k)-anonymity, and t-closeness. In this paper we introduce the EnhancedPKClustering algorithm for generating p-sensitive kanonymous microdata based on frequency distribution of sensitive attribute values. The p-sensitive k-anonymity model and its enhancement, extended psensitive k-anonymity, are described, their properties are presented, and two diversity measures are introduced. Our experiments have shown that the proposed algorithm improves several cost measures over existing algorithms.