# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 2 Privacy Preserving Data Publishing and Major Research Tracks
### Attribute-Centric and Synthetic-Data-Based Privacy-Preserving Methods  
In recent years, there has been an increasing focus on developing attribute-centric and synthetic-data-based privacy-preserving solutions. In the former category, the characteristics of the underlying data are exploited to strike the balance between privacy and utility [39]. In the latter category, virtual samples are generated to augment the data quality without compromising privacy [40]. In this paper, our main focus is on attribute-centric and synthetic-data-based privacy-preserving solutions that remained unexplored in the previous research. Figure 6 demonstrates the conceptual overview of both these methods. Referring to Figure 6a, the tabular data on the right side are real data, and the left side shows the anonymized data. In these data, the last column is a sensitive attribute (SA), and the first three columns are quasi-identifiers (QIDs). Referring to Figure 6a, the detailed information concerning the composition of the data is exploited to ease the anonymity process. For example, by classifying data and exploiting the co-relations between attributes, privacy and utility can be effectively preserved [41]. By exploiting information concerning data, computing power can also be saved, and only the relevant parts can be anonymized [42]. Referring to Figure 6a, the conditional generative adversarial (CTGAN) model is employed to create synthetic data by mimicking the properties of real data. In the CTGAN model, the important component is the conditional vector that guarantees the data generation of the best quality. Recently, many variants of GAN models were developed to curate data of diverse modalities (e.g., time series, images, tabular data, graphs, etc.) for different use cases [43, 44, 45]. Both these methods are very recent and significantly contribute to data outsourcing while preserving data privacy. In recent years, many privacy-preserving methods were developed to securely publish personal data. [PERSON] et al. [46] proposed a multidimensional data aggregation and privacy-preserving scheme based on the federated learning concept. The proposed scheme can be used in industrial IoT domains to restrict privacy breaches. [PERSON] et al. [47] discussed the security and privacy concerns in the healthcare sector due to the rapid rise in digital technologies. [PERSON] et al. [48] developed a privacy-preserving framework for self-diagnosis based on IoT devices. The proposed framework utilizes patient records and performs analysis without losing guarantees of privacy. [PERSON] et al. [49] proposed a blockchain-based framework for privacy preservation in IoT scenarios. [PERSON] et al. [50] proposed a novel method by amalgamating machine learning techniques and conditional probability distributions to effectively resolve the privacy-utility trade-off. The proposed method can be used to preserve privacy in single as well as multiple SA  
Figure 5: Overview of the data collection process used in this study. scenarios. [PERSON] et al. [51] discussed the PPDSM and PPDM methods used for privacy protection. The authors analyzed the strengths and weaknesses of the existing methods and stressed the need for more methods in the PPDSM domain in the future. [PERSON] et al. [52] proposed a method for privacy preservation in the image data. The authors used privacy protection methods with (convolutional, variational) autoencoders to preserve the privacy of the image data. [PERSON] et al. [53] developed a scheme to trace virus-infected people without exposing their privacy. The authors proposed a new IoT data management architecture and applied various perturbation techniques to effectively preserve privacy and availability in the cloud environments. [PERSON] et al. [54] proposed two methods (i.e., archive and data commons) for genomics data sharing. The authors highlighted the challenges involved in data sharing and discussed privacy and security concerns. [PERSON] et al. [55] proposed a method for privacy preservation in query execution.