Paper detail

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