# Challenges of Using Synthetic Data Generation Methods for Tabular Microdata
## 5 Discussion  
As evidenced by empirical studies and advancements in methodologies like ADS-GAN and PATE-GAN, several critical issues must be carefully navigated to ensure the efficacy and reliability of synthetic data across diverse datasets. Firstly, one of the foremost issues lies in the sensitivity of SDG models to training parameters such as epochs and batch sizes. Our study underscores the variability in model performance across different datasets based on these parameters. While models like TVAE exhibit more consistent performance with fewer parameter adjustments, GAN-based methods like CTGAN show greater sensitivity, necessitating robust algorithmic designs. Future research directions should focus on developing adaptive learning mechanisms and automated hyperparameter tuning strategies. These innovations can enhance the resilience of SDG methods to parameter variations, ensuring more reliable outcomes across various data scenarios. Scalability poses a hurdle in SDG, which is particularly evident when tackling larger and more diverse datasets. While statistical models like Copula and CopulaGAN offer simplicity, their effectiveness diminishes as the dataset complexity grows. Conversely, GAN-based methods leverage NNs, showing promise in scaling tasks, albeit demanding significant computational resources. This becomes particularly challenging with high-dimensional datasets rich in categorical variables, where GANs may falter. TVAE emerges as a compelling alternative, excelling in handling complex data while maintaining efficiency, which is crucial for practical applications. Moreover, integrating distributed training, parallel processing, and advanced optimization techniques holds the potential to bolster scalability, offering a pathway to address current limitations and foster wider adoption of synthetic data across diverse and intricate datasets. There are also issues with model robustness and generalization: GANs rely on the assumptions that the underlying data distribution remains consistent and that the trained models generalize well to unseen data. However, real-world data may exhibit complex and dynamic patterns, making it challenging for the models to generalize effectively. This is, of course, a general problem, but it is exacerbated here by the fact that we do not know what has been generalized from the data and what has not. This black-box nature makes it difficult to adjust GANs accordingly, as would be the case with parameterized statistical models, for example. This is one reason why so many different GANs are now available and why new GANs need to be trained for each case [35]. This is unsatisfactory in the long run. It would be beneficial to develop transfer models here, analogous to image classification, that generalize sufficiently for certain scenarios. Our empirical study also elucidates the intricate balance between privacy and utility in SDG. The findings reveal that the Copula model excels in privacy preservation with consistently low \(\epsilon\)-Identifiability, albeit at the cost of lower utility in most datasets, except the BC dataset. CopulaGAN and CTGAN offer a more balanced privacy-utility trade-off, with CopulaGAN showing higher \(\epsilon\)-Identifiability but also higher utility, particularly when tuned with larger batch sizes and more epochs. CTGAN, while similar, provides slightly better privacy with comparable utility. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance SDG. Ethical and regulatory considerations present significant challenges in the practical application of SDG techniques. Compliance with stringent data privacy regulations and ethical guidelines necessitates robust governance frameworks.