A comparative exploration of two diffusion generative models on tabular data synthesis

The generation of synthetic tabular data has become increasingly important as a solution to data accessibility, privacy, and resource constraints issues. Despite the development of various synthetic data generators, comprehensive evaluations of their effectiveness and generalizability are limited. This study aims to conduct a detailed comparative analysis of two notable diffusion models, TabDDPM and TabSyn, which are reputed for their ability to produce high-quality synthetic data but have not been extensively tested against each other in the context of tabular datasets. The research proposes a framework to evaluate the performance of TabSyn and TabDDPM models in terms of generating synthetic tabular data, maintaining statistical similarity with real datasets, and upholding data privacy. We utilized six datasets with varying dimensionality and assessed the generated data using the proposed framework including three key criteria: similarity, utility, and privacy preservation. Our findings indicate that TabSyn surpasses TabDDPM in similarity, privacy, and utility metrics for all datasets. The quality of data produced by the TabSyn model closely mirrors real datasets, showing strong statistical alignment for both categorical and continuous variables. Additionally, the utility of the synthetic data for machine learning applications is comparable to that of real tabular data. Privacy assessments confirm that the data generated by the TabSyn model maintain stringent privacy standards. The findings confirm TabSyn’s effectiveness and establish its superiority in generating synthetic data, providing guidance for practitioners in selecting synthetic data generation models and setting benchmarks for future innovations in the field. The results underscore the importance of adopting advanced generative models like TabSyn to create tabular data that not only accurately reflect real-world distributions but also protect individual privacy. Code has been made available at GitHub.