# Enhancing Utility in Anonymized Data against the Adversary's Background Knowledge
## 1 Introduction  
In recent years, Big Data and Internet of Things (IoT) technologies have received serious attention because of the advancement in information and communications. Individual data can provide valuable insights to recipients for making business decisions, such as iPhone, Facebook, Twitter, LinkedIn, and some government departments. However, sensitive information, such as personal details about an individual's health, bank accounts, insurance, etc., may be at risk of disclosure if the recipient performs any analysis on the collected data. Therefore, organizations have a responsibility to develop privacy techniques that protect individual identities while disseminating data. Privacy-preserving data publishing (PPDP) is a technique used for disseminating user logs in search engines, graph data, such as social networks, high-dimensional data, such as DNA strings, and patient data at various stages of their diseases while preserving privacy [1, 2, 3, 4, 5, 6]. In privacy-preserving data publishing (PPDP), data anonymization is one of the approaches used to protect users' sensitive information [2]. Anonymization refers to a modified version of the original microdata that does not display confidential personal information or any relations between individuals and records in the data. The simplest approach involves removing identity characteristics, such as names and national IDs of individuals [2]. However, this technique has been proven insecure [7]. [PERSON] [7]demonstrated that it is possible to identify 87% of US residents using a combination of their date of birth, five-digit zip code, and gender. The challenge of privacy preservation during data publishing involves releasing a modified form of the data that does not reveal identities or sensitive information (i.e., data are no longer personal). It is worth noting that reducing the accuracy of data analysis on anonymous data is inevitable and results in information loss. Anonymization frameworks typically consist of an anonymization algorithm and a privacy model. Privacy models can be classified into two categories: syntactic and semantic. Syntactic models divide data into several groups, called quasi identifier (QI) groups, such that records in each QI group share the same QI attribute values (Assuming that data are stored in a relational data model, each record has three types of attributes: identifiers, QIs, and sensitive attributes (SA). For instance, in a patient dataset, the name is an identifier attribute, while age, gender, and geographic location are QI attributes, and disease is an SA). The initial syntactic model is \(k\)-anonymity [7], where each record must be similar to at least \(k\)-1 other records. While this model prevents identity disclosure, it remains vulnerable to attribute disclosure [7]. Several refinements of \(k\)-anonymity, such as \(t\)-closeness [8] and \(l\)-diversity [9], have been proposed to address attribute disclosure. In semantic models, noise is added to attribute values to protect individuals' privacy [10]. Each anonymization framework gives protection against a special adversary model. The adversary model includes assumptions about its knowledge. It is often assumed that (1) the adversary's victims are in the data and (2) the victims' QI attribute values are known [1; 2; 3; 4; 5; 6; 7; 8; 9]. Background knowledge (BK) is an additional fact that can help the adversary make accurate inferences on the victim's SA, named a BK attack. This work assumes the adversary's correlational background knowledge (CBK) about the correlations between SA values and QI values. Suppose a hospital publishes patient data anonymously to analyze the progression of diseases (A hospital may publish data on patients for various purposes. For example, it publishes data to analyze the effectiveness of a specific drug in stopping or slowing the disease's progression). CBK in medicine and healthcare can show the impact of age and gender (or individuals' geographical location) on the prevalence of various diseases.