# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
###### Abstract
For example, medical data can be regarded as poor quality when a medical student interested in exploring the disease connection with various demographics cannot find relevant information from the data. Similarly, if the data do not encompass diverse and complete information, they are also referred to as poor quality. The quality of the data from within the utilization perspective varies from application to application and case to case. In contrast, a dataset that is complete, diverse, and representative of the problem under investigation is considered to be of rich quality. Therefore, the misuse of personal data can be prevented by applying careful anonymization and curating more data to improve the bad parts of the data (e.g., the data concerning minor population groups where the risk of discrimination is high). However, the optimization of the privacy-utility trade-off, the prevention of the misuse of data, and the improvement in data quality are very challenging to achieve simultaneously. Most of the existing methods often pay attention to one metric and compromise the others. It is vital to achieve all three objectives simultaneously by paying careful attention to data properties, data pre-processing, and anonymization. To address the challenges of the privacy versus utility trade-off, a variety of anonymization methods were developed. The majority of the developed methods are derived from the four methods, such as \(k\)-anonymity [4], \(\ell\)-diversity [5], \(t\)-closeness [6], and differential privacy [7]. Apart from these mainstream solutions, many enhancements of these methods were proposed to optimize the privacy and utility in personal data handling [8, 9, 10]. To prevent the misuse of personal data, techniques such as synthetic data, legal measures, consents, encryption, and other privacy-enhancing technologies, etc., are used [11, 12, 13]. Recently, some work has begun on data quality enhancement to improve decision-making and to solve industrial problems [14, 15]. The data-tailored practices are vital to improving the quality of people's lives with improved decision-making. Unfortunately, there is a serious lack of data-centric methods in the privacy domain, and only a few methods have considered the properties of data during their anonymization. In addition, none of the previous studies have highlighted the need for data- or attribute-centric methods in the privacy domain. The major contributions of this paper are summarized below. * We discuss the major research tracks in the information privacy domain with a specific emphasis on attribute-centric anonymization methods that were recently developed to address the privacy versus utility trade-off. * We discuss synthetic data generation methods and the role of synthetic data as a privacy-enhancing technology, as well as a data quality enhancement technology. * We highlight the various privacy-enhancing technologies that are widely used to preserve the privacy of the personal data enclosed in heterogeneous formats. * We suggest promising research tracks for future work that require the immediate attention of the privacy community amid the rapid rise in digitization. * To the best of the authors' knowledge, this is the first work that discusses two feature-oriented privacy-enhancing technologies (i.e., attribute-centric and synthetic data) from a much broader perspective. We hope to provide a solid foundation for future research by making a timely contribution to this line of work. There exist plenty of metrics for comparing synthetic data generation quality from multiple perspectives. [PERSON] [16] suggested seven different ways to gauge the quality of synthetic data in real-world cases. Mostly, the distribution and structural similarities are analyzed to check the closeness between the synthetic and real data. In some cases, the utility of the synthetic data is evaluated with respect to the target application (e.g., health care) for which the data are being curated [17].