Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques

TL;DR

This survey reviews recent methods for generating synthetic tabular data, focusing on preserving data utility, privacy, and complex feature relationships, and introduces a taxonomy and benchmark framework to guide future research and practical applications.

Contribution

It introduces a novel taxonomy based on generation objectives and proposes a benchmark framework, bridging theoretical methods with real-world privacy and utility needs.

Findings

Highlights the importance of preserving feature relationships

Emphasizes privacy guarantees in synthetic data generation

Provides a benchmark for evaluating synthetic tabular data methods

Abstract

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.