# Challenges of Using Synthetic Data Generation Methods for Tabular Microdata
## 1 Introduction  
Despite initiatives for open-linked data and the enforcement of FAIR (Findable, Accessible, Interoperable, Reusable) principles, there is still a lack of publicly available microdata for training machine learning models [1, 2]. One issue is the uncertainty regarding the associated privacy breach risk, which is primarily addressed through technical and organizational measures such as the establishment of a scientific review board, federated learning, and signing of terms-of-use clauses [3, 4]. While this reduces the risk, it results in researchers having to reapply repeatedly for datasets and being unable to create a larger collection of data across different institutions. Synthetic data generation (SDG) promises a viable solution for this purpose and is driven by two distinct communities [5, 6]: on the one hand, the statistical community, which already leans towards synthetic data with multiple imputation and requires explicit modeling of relationships in the data [7, 8]; and on the other hand, the computer science community, which attempts to generate synthetic data without the modeling effort by using neural networks (NNs) as approximators of real-world data distributions [9, 10]. A significant challenge lies in ensuring that syntheticdata accurately represent the statistical properties of the original data. Synthetic data may fail to capture all the nuances of the original dataset, leading to potential inaccuracies in representation. This can result in discrepancies in distributions and correlations, which are crucial for maintaining the integrity of data-driven research. Therefore, it is imperative to employ advanced methods and rigorous validation techniques to ensure that synthetic data preserves the essential statistical properties of the original data. Statisticians often rely on explicit modeling of relationships within the data. Two prominent R packages in this context are synthpop [11] and simpop [12]. By explicitly modeling the data, statisticians can ensure that the distributions, correlations, and other statistical properties are preserved. They validate the synthetic data through rigorous statistical tests and comparisons with the original data, adjusting their models as necessary to achieve high fidelity. This approach allows for a clear understanding and control over the generated data, ensuring that it reflects the original data's nuances accurately. On the other hand, computer scientists leveraging NNs employ a more automated, black-box approach to SDG. NNs, particularly Generative Adversarial Networks (GANs) and their variants, are trained to learn the underlying patterns and distributions of the original data without the need for explicit modeling. The sophisticated black-box nature of NNs allows them to capture complex, non-linear relationships that might be difficult to model explicitly. However, this approach also requires careful tuning and validation to ensure the generated data are accurate. Both approaches have their strengths: the statistical methods provide transparency and control, while NNs offer the ability to capture complex patterns with less manual intervention. GANs are currently one of the most promising NNs for generating synthetic data that are difficult to model explicitly and operate according to the Turing test principle [13; 14]. GANs consist of two NNs: a generator and a discriminator. The generator creates candidates, in our case a synthetic dataset, by way of learning to mimic the real data distribution. The discriminator, on the other hand, attempts to distinguish the generator's results from the real data. Over multiple iterations, the generator and discriminator are trained in competition with each other: the generator aims to produce increasingly better synthetic datasets that closely resemble the real data, while the discriminator is trained to better distinguish the generator's results from the real data. The goal of this process is for the generator to produce synthetic data that the discriminator cannot distinguish from real data.