# Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration

arXiv: https://arxiv.org/abs/2008.12763 · HTML: https://mlsanity.com/paper/2008.12763/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [FinDiff: Diffusion Models for Financial Tabular Data Generation](https://mlsanity.com/paper/2309.01472/) (2023)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data](https://mlsanity.com/paper/2404.15821/) (2024)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Diffusion Models for Tabular Data Imputation and Synthetic Data Generation](https://mlsanity.com/paper/2407.02549/) (2024)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models](https://mlsanity.com/paper/2404.03299/) (2024)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Tabular Data Synthesis with Differential Privacy: A Survey](https://mlsanity.com/paper/2411.03351/) (2024)
- [Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities](https://mlsanity.com/paper/2402.01071/) (2024)
- [Poisoning Network Flow Classifiers](https://mlsanity.com/paper/2306.01655/) (2023)
- [AnnotatedTables: A Large Tabular Dataset with Language Model Annotations](https://mlsanity.com/paper/2406.16349/) (2024)
- [NRGBoost: Energy-Based Generative Boosted Trees](https://mlsanity.com/paper/2410.03535/) (2024)
- [Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data](https://mlsanity.com/paper/2504.21634/) (2025)
- [DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks](https://mlsanity.com/paper/2508.21340/) (2025)
- [Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning](https://mlsanity.com/paper/2212.03423/) (2022)
- [Measuring LLM Sensitivity in Transformer-based Tabular Data Synthesis](https://mlsanity.com/paper/2509.20768/) (2025)
- [What-If Decision Support for Product Line Extension Using Conditional Deep Generative Models](https://mlsanity.com/paper/2511.11646/) (2025)
- [Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models](https://mlsanity.com/paper/2607.24829/) (2026)

## Abstract (arXiv)

The proliferation of big data has brought an urgent demand for privacy-preserving data publishing. Traditional solutions to this demand have limitations on effectively balancing the tradeoff between privacy and utility of the released data. Thus, the database community and machine learning community have recently studied a new problem of relational data synthesis using generative adversarial networks (GAN) and proposed various algorithms. However, these algorithms are not compared under the same framework and thus it is hard for practitioners to understand GAN's benefits and limitations. To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates applying GAN to relational data synthesis. We introduce a unified GAN-based framework and define a space of design solutions for each component in the framework, including neural network architectures and training strategies. We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches. Through extensive experiments, we find that GAN is very promising for relational data synthesis, and provide guidance for selecting appropriate design solutions. We also point out limitations of GAN and identify future research directions.