# Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques

arXiv: https://arxiv.org/abs/2507.11590 · HTML: https://mlsanity.com/paper/2507.11590/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2025.

## Built on by (most cited first, 6 of 6)

- [Causal-Aware Tabular GANs with Reinforcement Learning](https://mlsanity.com/paper/2510.24046/) (2025)
- [GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models](https://mlsanity.com/paper/2608.18849/) (2026)
- [Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN](https://mlsanity.com/paper/2508.06647/) (2025)
- [SecretFan: Synthesizing Realistic Data without Breaking Privacy](https://mlsanity.com/paper/2602.05833/) (2026)
- [Generating High-quality Privacy-preserving Synthetic Data](https://mlsanity.com/paper/2602.06390/) (2026)
- [LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation](https://mlsanity.com/paper/2608.01879/) (2026)

## Abstract (arXiv)

As privacy regulations become more stringent and access to real-world data becomes increasingly constrained, synthetic data generation has emerged as a vital solution, especially for tabular datasets, which are central to domains like finance, healthcare and the social sciences. This survey presents a comprehensive and focused review of recent advances in synthetic tabular data generation, emphasizing methods that preserve complex feature relationships, maintain statistical fidelity, and satisfy privacy requirements. A key contribution of this work is the introduction of a novel taxonomy based on practical generation objectives, including intended downstream applications, privacy guarantees, and data utility, directly informing methodological design and evaluation strategies. Therefore, this review prioritizes the actionable goals that drive synthetic data creation, including conditional generation and risk-sensitive modeling. Additionally, the survey proposes a benchmark framework to align technical innovation with real-world demands. By bridging theoretical foundations with practical deployment, this work serves as both a roadmap for future research and a guide for implementing synthetic tabular data in privacy-critical environments.