# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 2 Privacy Preserving Data Publishing and Major Research Tracks  
### Privacy Preserving Data Publishing  
In this section, we explain the privacy-preserving data publishing (PPDP) process and outline its major steps. Furthermore, we also discuss the major research tracks of the PPDP. A conceptual overview of PPDP to foster the secondary use of personal data is demonstrated in Figure 2.  
The whole PPDP process encompasses five key steps. In the first step, the data are collected from the relevant people. The collected data can be of any type such as demographics, information about monthly earnings, disease contracted, etc. In the second step, the data are stored, and pre-processed (the pre-processing includes data cleaning and quality enhancement (removing redundant records, removing outliers, handling missing values, and giving simplified structure to the data)) in the data owners' environments. The data owners can be hospitals, banks, insurance companies, social network service providers, etc. Later, the data are anonymized with the help of generalization hierarchies or the noise addition of DP. In the fourth step, the data are outsourced to data analysts or miners to extract the enclosed knowledge. The data are published either directly by the data owners or with the help of third parties. In the fifth step, the published data are analyzed by analysts or data miners with the help of advanced data mining or machine  
Figure 2: Schematic of privacy-preserving data publishing (PPDP) for the secondary use of data. learning tools. In the last step, the knowledge gained from the published data is used to improve real-world services such as healthcare, smart cities, recommendation systems, navigation services, and other data-driven services [30, 31]. Data sharing has become a routine matter amid the rapid rise in digital developments. In addition, data sharing is imperative to improve the quality of data-driven applications/services in the modern era. Although a substantial number of studies were proposed to address privacy issues in PPDP, there are still two major trade-offs that are hard to achieve. We present both trade-offs in Figure 3. In the first trade-off, the semantics of the original data need to be preserved [32]. In contrast, for the second trade-off, it is desirable to prevent minor values from dilution during the anonymization [33]. Despite many developments, both these trade-offs are quite challenging to achieve in the PPDP scenarios. The effective resolution of the privacy-utility trade-off is hard to achieve owing to strict privacy parameters to be applied during anonymization, poor quality of the underlying data, strict privacy/utility guarantees, and lack of evaluation metrics. In some cases, the selection of the optimal level/\(e\) to perform data generalization/transformation leads to the solution of one metric, i.e., either utility or privacy [34]. Similarly, the privacy-equity trade-off cannot be achieved owing to the poor quality (e.g., data imbalance) of the original data. For example, if there are fewer representations of some minor groups in the real data, the anonymization method further reduces the representation, leading to fewer benefits for some minority groups. Most anonymization methods do not improve data quality before anonymization, and therefore, it is very hard to achieve the trade-off between privacy and equity. In the modern era, it was suggested to improve data quality to lower the data-driven threats [14]. In conclusion, there are many obvious reasons (e.g., poor data quality, inappropriate anonymization, data owner requirement, privacy and utility objectives, data publishing goals, wrong data collection processes, and malicious entities present in the PPDP process) that make these trade-offs hard to achieve. All three trade-offs interact with each other because the data portion, which contains more information for analytics/mining, may require better privacy preservation.