Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques

2025-07-15

AI Summary

Facing increasingly stringent privacy regulations and restricted access to real-world data, this paper systematically surveys tabular data synthesis techniques for high-stakes domains such as finance and healthcare, focusing on three core challenges: privacy preservation, statistical fidelity, and modeling of complex variable dependencies. Methodologically, we propose a generation-objective-driven taxonomy that categorizes approaches by downstream task adaptability, differential privacy guarantees, and data utility trade-offs; emphasize conditional generation and risk-aware modeling to bridge the theory–practice gap; and unify generative models—including GANs, VAEs, and diffusion models—with differential privacy mechanisms within a cohesive evaluation framework integrating statistical metrics and privacy quantification tools. Our contributions include a reproducible benchmark covering state-of-the-art methods and a practical technology selection guide, enabling secure deployment of high-fidelity, privacy-preserving synthetic data in sensitive applications.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Privacy
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Survey modern techniques for synthetic tabular data generation
• Preserve feature relationships and statistical fidelity in synthetic data
• Address privacy and utility in synthetic data for critical domains

Innovation

• Taxonomy based on practical generation objectives
• Benchmark framework aligning tech with demands
• Methods preserving feature relationships and privacy