# Enhancing Utility in Anonymized Data against the Adversary's Background Knowledge
## 1 Introduction
Examples of BK in a medical domain are \"the prevalence of Bronchitis and Alzheimer's diseases among women 65 years old and older is higher than the prevalence among men in the same age group\", \"breast cancer in men is rare\", etc. [11]. The work in this paper proposes a syntactic anonymization framework that enhances the data utility and prevents attribute disclosure, identity disclosure, and BK attack. When a QI group is released, the adversary can calculate the likelihood of potential connections between the individual and their sensitive information. In the absence of any background knowledge, in cases where there are multiple distinct sensitive values within the QI group, the probability of linking a record respondent to a specific sensitive value is equivalent. Modeling the adversary's BK is an open challenge in PPDP [6; 12]. The proposed privacy model aims to minimize certainties in distinguishing record owners and their sensitive values in a QI group. We attempt to produce QI groups where patient records have similar BK distributions to prevent BK attacks. Therefore, adversaries will have reduced confidence in their ability to identify any specific association between a victim and their sensitive values. Ensuring similar BK distributions helps prevent CBK attacks, but it does not provide protection against attribute and identity disclosure. To meet the privacy goals of preventing attribute and identity disclosure, it is crucial to also ensure \(k\)-anonymity and \(t\)-closeness in each QI group. Therefore, our proposed framework produces QI groups that satisfy three privacy requirements: (1) similar BK distributions, (2) \(k\)-anonymity, and (3) \(t\)-closeness. The proposed algorithm uses value generalization. A hierarchical procedure is implemented to fulfill the requirements of the proposed privacy model. Firstly, it uses agglomerative clustering to create clusters with the constraint that the difference of BK distributions between any pair of records within a cluster does not exceed a predefined threshold. Next, the anonymization algorithm partitioned each cluster into QI groups satisfying both \(t\)-closeness and \(k\)-anonymity requirements. Finally, records within each QIgroup are reordered to enhance the similarity of QI values, thereby improving the utility of the anonymized data. We performed comprehensive experiments using two datasets, namely the adult dataset [13] and the BKseq dataset [6] (Accessed January 2023), to assess the efficacy of our anonymization platform. The evaluation assessed the proposed algorithm against a range of privacy and information loss measures. The results confirm that our algorithm creates anonymous data with high data utility. Moreover, we compared the proposed algorithm with two state-of-the-art anonymization algorithms: the \(k\)-anonymity-primacy algorithm [12] and the BK-based algorithm [6]. The experimental results reveal that the proposed algorithm outperforms the state-of-the-art anonymization algorithms because of low information loss and privacy loss. We have made the following main contributions:  
1-We propose an anonymization framework that simultaneously satisfies \(t\)-closeness and \(k\)-anonymity. Our framework also protects against adversaries with knowledge on correlations of attribute values. 2-We propose a generalization-based anonymization algorithm that satisfies the privacy models and improves data utility. To this end, the records are moved between the QI groups to maximize the usefulness of resultant anonymous data. We conduct comprehensive experiments on various aspects of the algorithms, including the runtime, privacy loss, data utility, and QI groups' size. The rest of this paper is organized as follows. Section 2 demonstrates the works related to the privacy model, anonymization algorithm, and the adversary's BK. In Section 3, we define the problem, which includes the adversary's BK. Section 4 represents our anonymization framework containing an anonymization algorithm and a privacy model.