Local generalization and bucketization technique for personalized privacy preservation

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories attribute: explicit-identifier, quasi-identifier (QI), sensitive attribute. Actually, different individuals may have view on the sensitivity attributes. Therefore, there is another type attribute that contains both QI values values, namely, semi-sensitive Based such observation, we propose new anonymization technique, called local generalization bucketization, to prevent identity disclosure protect each The rationale use bucketization divide tuples into equivalence groups partition buckets, respectively. protections are independent, so they can be implemented by appropriate algorithms without weakening other protection, Besides, protection also independent. Consequently, comply with various principles in attributes according actual requirements anonymization. conducted extensive experiments illustrate effectiveness proposed approach.