# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
###### Abstract
A comprehensive list of metrics that can be used to evaluate the quality of synthetic data can be learned from [PERSON] et al. [18]. In some cases, the evaluation of synthetic data is performed from the perspectives of both privacy and utility [19]. In most of the existing papers, the comparison regarding the quality of the synthetic data is made with real data or the previous algorithms that were proposed for similar tasks (e.g., synthetic data generation). Similarly, the requirements for the attribute-centric methods are to exploit any useful knowledge that can either lessen the computing complexity of privacy models or assist in effectively resolving the trade-offs between privacy and utility. Only a limited number of papers have been proposed in this line of work that exploit valuable knowledge about real data to improve privacy/utility results [20, 21]. In [20], the authors considered the preferences of users along with their data at anonymization time to improve data quality in data publishing scenarios. In [21], the authors devised a method to rank the attribute first, and then, \(KNN\) clustering was applied to enable the anonymity of the datasets lacking diversity. The proposed method reduces the overgeneralization issues, thereby achieving privacy protection for all attributes. The major requirement for the attribute-centric methods is any piece of knowledge regarding real data composition or attributes' values that can ease the anonymity process. The empirical and formal comparison can be made to contrast these requirements and accomplishments towards these requirements in personal data handling scenarios [22, 23]. The use cases discussed in these studies are privacy protection when a database encompasses more than one SA and permutation of the dataset for fulfilling \(k\)-anonymity criteria, respectively. Feature-based comparisons between traditional and attribute-centric methods can also be performed to highlight the novel aspects of the attribute-centric methods. There exists a plethora of survey articles on privacy-preserving data mining (PPDM) for a variety of applications such as cloud computing, location-based services, e-health, recommender systems, transport data, and internet of things [24, 25, 26, 27, 28]. In PPDM, privacy is ensured by applying the anonymization method while minimally changing the semantics of the data [25]. The objective of PPDM is to maximize utility with considerable privacy guarantees. In PPDM, the data are not released publicly in some cases, and instead, queries are executed in a privacy-preserved way. On the other hand, PPDP explores ways to publish anonymized data either in full or partial form so that they can be used by relevant information consumers [29]. This work presents the latest developments in the PPDP area and has two main differences from the existing surveys. First, we identify and discuss the data generation methods along with the privacy guarantees (e.g., GAN and DP are simultaneously used or GAN is used first to curate the data, which are later anonymized via DP or general anonymity solutions) to compensate for the deficiency in the data. The privacy-preserved synthetic data can also be used to train AI models while providing privacy guarantees against some threats such as membership inference attacks. Second, we describe the attribute-centric methods that extract some knowledge from the data composition to ease the anonymization process. To the best of our knowledge, both of these methods have not been thoroughly covered in the previous surveys. To cover this research gap, our survey can provide a solid foundation for future studies in these lines of work (e.g., synthetic-data-based privacy-preserving methods and attribute-centric anonymization methods). Finally, we provide sufficient experimental details of the previous studies concerning these two types of methods, which can help researchers quickly grasp the research status and further advance the status of these developments. The rest of this paper is structured as follows.