Enhancing Utility in Anonymized Data against the Adversary’s Background Knowledge

Abstract

Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time the modified data must have sufficient accuracy for answering data mining queries. However, it is difficult to implement sufficient privacy where re-identification of a record is claimed to be impossible because the adversary has background knowledge from different sources. The k-anonymity model is prone to attribute disclosure, while the t-closeness model does not prevent identity disclosure. Moreover, both models do not consider background knowledge attacks. This paper proposes an anonymization algorithm called the utility-based hierarchical algorithm (UHRA) for producing k-anonymous t-closed data that can prevent background knowledge attacks. The proposed framework satisfies the privacy requirements using a hierarchical approach. Finally, to enhance utility of the anonymized data, records are moved between different anonymized groups, while the requirements of the privacy model are not violated. Our experiments indicate that our proposed algorithm outperforms its counterparts in terms of data utility and privacy.