# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 4 Discussion on Synthetic Data-Based Privacy Methods  
Recently, synthetic data have been widely used to preserve the privacy of users while fulfilling the data analytics needs [87, 88]. In recent years, a large amount of data has been needed to train AI models for enhancing accuracy, particularly in the healthcare domain [89]. To this end, synthetic data can be employed to increase the number of samples in training data. Synthetic data have become a SOTA technology with a wide range of practical applications in diverse sectors. Figure 7 presents the practical uses of synthetic data in the modern era. From Figure 7, it can be seen that synthetic data are widely used as a replacement for real data in diverse sectors. The application of synthetic data in the healthcare sector is also steadily increasing with time [90]. [PERSON] et al. [18] recently presented a comprehensive survey on different tools that can be used to generate synthetic data (in a tabular form), particularly generative adversarial network (GAN) architectures. [PERSON] et al. [91] recently discussed the innovative uses of synthetic data in healthcare. Based on these major developments, it is fair to say that synthetic data are one of the leading technologies of the future. This paper focuses on privacy preservation, and therefore, we demonstrate synthetic-data-based privacy methods. This topic has attracted the researchers' attention in recent years, and many notable developments have been made [92]. Table 3 presents the comparison of SOTA synthetic-data-based privacy methods. Specifically, we compare each method on five grounds (i.e., techniques used in the study, objective accomplished with the study, type of the study (technical or theoretical), and data type used in each study). Furthermore, we considered the most recent studies to provide a fresh analysis of the published literature. The analysis presented in Table 3 can pave the way to understanding the latest developments in synthetic-data-based privacy methods. Each method listed in Table 2 has used a different mechanism to generate synthetic data of good quality to either preserve the privacy of personal data or to fulfill data requirements/needs. The commonly used mechanisms in these studies are differential privacy, probabilistic modeling, variational autoencoders, generative adversarial networks, neural networks, generative models, pipelines including differential privacy, clustering methods, and transformer models. In conclusion, most of these methods have used basic generative adversarial networks (GANs) and their enhancement to generate data of diverse types. In these methods, privacy is achieved using two ways: (i) including DP or any other privacy models with the generative models, and (ii) generating synthetic data and applying anonymization to guarantee privacy. The synthetic-data-based methods are widely used to accomplish five key requirements in the modern era: (i) privacy preservation of personal data, (ii) testing of software/products, (iii) data governance, (iv) data sharing at a scale, and (v) training machine learning classifiers [93]. Figure 7: Practical uses of synthetic data in the modern era (e.g., the year 2023 and beyond).