# Privacy-preserving tabular data synthesis

Area: [Synthetic data and self-improvement for LLMs](https://mlsanity.com/synth/index.md). HTML: https://mlsanity.com/synth/privacy-preserving-tabular-data-synthesis/

## Key facts

- Privacy-preserving tabular data synthesis: 53 papers on arXiv since 2021 (data through 14 Oct 2026).
- 12 of its 53 mapped papers (23%) cite “Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space” (2023, arXiv 2310.09656).
- 9 of its 53 mapped papers (17%) cite “Differentially Private Synthetic Data via Foundation Model APIs 2: Text” (2024, arXiv 2403.01749).
- 2 new papers 17 Sep–14 Oct.

## Most cited here

- [Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space](https://mlsanity.com/paper/2310.09656/) (2023), cited by 12 papers here
- [Differentially Private Synthetic Data via Foundation Model APIs 2: Text](https://mlsanity.com/paper/2403.01749/) (2024), cited by 9 papers here
- [Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion](https://mlsanity.com/paper/2502.00245/) (2025), cited by 4 papers here
- [TabuLa: Harnessing Language Models for Tabular Data Synthesis](https://mlsanity.com/paper/2310.12746/) (2023), cited by 3 papers here
- [Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model](https://mlsanity.com/paper/2502.05505/) (2025), cited by 3 papers here
- [POPri: Private Federated Learning using Preference-Optimized Synthetic Data](https://mlsanity.com/paper/2504.16438/) (2025), cited by 3 papers here
- [EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models](https://mlsanity.com/paper/2404.12404/) (2024), cited by 2 papers here
- [ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models](https://mlsanity.com/paper/2405.17724/) (2024), cited by 2 papers here
- [Systematic Assessment of Tabular Data Synthesis](https://mlsanity.com/paper/2402.06806/) (2024), cited by 1 papers here
- [A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models](https://mlsanity.com/paper/2404.14445/) (2024), cited by 1 papers here

## New (17 Sep–14 Oct)

- [Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data](https://mlsanity.com/paper/2609.38414/) (2026-09-29)
- [TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models](https://mlsanity.com/paper/2609.37989/) (2026-09-29)