# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
###### Abstract  
Anonymization techniques are widely used to make personal data broadly available for analytics/data-mining purposes while preserving the privacy of the personal information enclosed in it. In the past decades, a substantial number of anonymization techniques were developed based on the famous four privacy models such as \(k\)-anonymity, \(\ell\)-diversity, \(t\)-closeness, and differential privacy. In recent years, there has been an increasing focus on developing attribute-centric anonymization methods, i.e., methods that exploit the properties of the underlying data to be anonymized to improve privacy, utility, and/or computing overheads. In addition, synthetic data are also widely used to preserve privacy (privacy-enhancing technologies), as well as to meet the growing demand for data. To the best of the authors' knowledge, none of the previous studies have covered the distinctive features of attribute-centric anonymization methods and synthetic data based developments. To cover this research gap, this paper summarizes the recent state-of-the-art (SOTA) attribute-centric anonymization methods and synthetic data based developments, along with the experimental details. We report various innovative privacy-enhancing technologies that are used to protect the privacy of personal data enclosed in various forms. We discuss the challenges and the way forward in this line of work to effectively preserve both utility and privacy. This is the first work that systematically covers the recent development in attribute-centric and synthetic-data-based privacy-preserving methods and provides a broader overview of the recent developments in the privacy domain. anonymization; personal data; \(k\)-anonymity; \(\ell\)-diversity; \(t\)-closeness; differential privacy; synthetic data; privacy enhancing technologies; privacy; attribute-centric anonymization; utility +
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As shown in Figure 1, a substantial number of users can be identified from the datasets, and many sophisticated techniques are required to address the privacy issues. There are three re-identification scenarios in Figure 1. In the first scenario, the much higher re-identification is due to the zip code, which precisely locates many individuals. In the second and third cases, the re-identification is lower due to the generalization of residence. These results highlight the need for effective privacy preservation in data analysis. The privacy preservation of data while offering higher knowledge is a global problem, and many techniques were developed to address this trade-off [3]. There are three important challenges while handling personal data encompassing basic as well as sensitive information about individuals. * Resolution of privacy versus utility trade-off: How to safeguard user privacy while still allowing data miners/analysts to maximally extract the enclosed knowledge from the personal data. * Preventing the misuse of personal data: How to enable fair and impartial decision making concerning real-world entities, and restricting target profiling (or discrimination about a minor community). * Enhancing the quality of personal data for the well-being of societies: How to improve the quality of the data when they are either small or of low quality to enable better data mining and decision making. It is important to note that all of the above-cited challenges are closely related to each other. For example, if the trade-off between privacy and utility is effectively resolved, the misuse of personal data can be restrained, and the personal data can be used as intended. Privacy disclosures occur when either inappropriate anonymization models are used in anonymization or the underlying dataset to be anonymized is of poor quality. A dataset is considered poor quality from the utilization perspective when the intended knowledge cannot be extracted from it with minimal effort.