# Benchmarking Differentially Private Tabular Data Synthesis

arXiv: https://arxiv.org/abs/2504.14061 · HTML: https://mlsanity.com/paper/2504.14061/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2025.

## Built on by (most cited first, 14 of 14)

- [Synthetic Tabular Data: Methods, Attacks and Defenses](https://mlsanity.com/paper/2506.06108/) (2025)
- [ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control](https://mlsanity.com/paper/2510.18232/) (2025)
- [Private Evolution Converges](https://mlsanity.com/paper/2506.08312/) (2025)
- [Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees](https://mlsanity.com/paper/2510.16974/) (2025)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data](https://mlsanity.com/paper/2602.02766/) (2026)
- [FHAIM: Fully Homomorphic AIM For Private Synthetic Data Generation](https://mlsanity.com/paper/2602.05838/) (2026)
- [Tight Auditing of Differential Privacy in MST and AIM](https://mlsanity.com/paper/2604.18352/) (2026)
- [Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis](https://mlsanity.com/paper/2511.13893/) (2025)
- [Evaluating LLM Simulators as Differentially Private Data Generators](https://mlsanity.com/paper/2604.15461/) (2026)
- [ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?](https://mlsanity.com/paper/2606.01849/) (2026)
- [Differentially Private Paired Table-Image Multimodal Synthesis](https://mlsanity.com/paper/2609.00708/) (2026)
- [PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis](https://mlsanity.com/paper/2609.39630/) (2026)
- [Tight End-to-End Accounting for Differentially Private Synthetic Data](https://mlsanity.com/paper/2610.17249/) (2026)

## Abstract (arXiv)

Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The emergence of diverse algorithms in recent years has introduced challenges in practical applications, such as inconsistent data processing methods, the lack of in-depth algorithm analysis, and incomplete comparisons due to overlapping development timelines. These factors create significant obstacles to selecting appropriate algorithms. In this paper, we address these challenges by proposing a benchmark for evaluating tabular data synthesis methods. We present a unified evaluation framework that integrates data preprocessing, feature selection, and synthesis modules, facilitating fair and comprehensive comparisons. Our evaluation reveals that a significant utility-efficiency trade-off exists among current state-of-the-art methods. Some statistical methods are superior in synthesis utility, but their efficiency is not as good as most deep learning-based methods. Furthermore, we conduct an in-depth analysis of each module with experimental validation, offering theoretical insights into the strengths and limitations of different strategies. Our code is open-sourced via the link.footnotehttps://github.com/KaiChen9909/tabbench