# Differentially Private Synthetic Data Generation for Relational Databases

arXiv: https://arxiv.org/abs/2405.18670 · HTML: https://mlsanity.com/paper/2405.18670/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 79 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 30 of 37)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data](https://mlsanity.com/paper/2201.12677/) (2022)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [Graphical-model based estimation and inference for differential privacy](https://mlsanity.com/paper/1901.09136/) (2019)
- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [Relational Deep Learning: Graph Representation Learning on Relational Databases](https://mlsanity.com/paper/2312.04615/) (2023)
- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [Supervised Learning on Relational Databases with Graph Neural Networks](https://mlsanity.com/paper/2002.02046/) (2020)
- [Composition of Differential Privacy & Privacy Amplification by Subsampling](https://mlsanity.com/paper/2210.00597/) (2022)
- [New Oracle-Efficient Algorithms for Private Synthetic Data Release](https://mlsanity.com/paper/2007.05453/) (2020)
- [Leveraging Public Data for Practical Private Query Release](https://mlsanity.com/paper/2102.08598/) (2021)
- [Differentially Private Synthetic Data: Applied Evaluations and Enhancements](https://mlsanity.com/paper/2011.05537/) (2020)
- [Private Synthetic Data for Multitask Learning and Marginal Queries](https://mlsanity.com/paper/2209.07400/) (2022)
- [ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models](https://mlsanity.com/paper/2405.17724/) (2024)
- [Dual Query: Practical Private Query Release for High Dimensional Data](https://mlsanity.com/paper/1402.1526/) (2014)
- [Kamino: Constraint-Aware Differentially Private Data Synthesis](https://mlsanity.com/paper/2012.15713/) (2020)
- [ARM-Net: Adaptive Relation Modeling Network for Structured Data](https://mlsanity.com/paper/2107.01830/) (2021)
- [Private Post-GAN Boosting](https://mlsanity.com/paper/2007.11934/) (2020)
- [Towards Practical Differential Privacy for SQL Queries](https://mlsanity.com/paper/1706.09479/) (2017)
- [Generating Private Synthetic Data with Genetic Algorithms](https://mlsanity.com/paper/2306.03257/) (2023)
- [Private GANs, Revisited](https://mlsanity.com/paper/2302.02936/) (2023)
- [GPU Acceleration of ADMM for Large-Scale Quadratic Programming](https://mlsanity.com/paper/1912.04263/) (2019)
- [Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning](https://mlsanity.com/paper/1912.03250/) (2019)
- [Algorithmically Effective Differentially Private Synthetic Data](https://mlsanity.com/paper/2302.05552/) (2023)
- [PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy](https://mlsanity.com/paper/2304.04545/) (2023)
- [Privacy of synthetic data: a statistical framework](https://mlsanity.com/paper/2109.01748/) (2021)
- [Post-processing Private Synthetic Data for Improving Utility on Selected Measures](https://mlsanity.com/paper/2305.15538/) (2023)
- [Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders](https://mlsanity.com/paper/2211.16889/) (2022)

## Built on by (most cited first, 2 of 2)

- [RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models](https://mlsanity.com/paper/2506.00710/) (2025)
- [Differentially Private Synthetic Data via APIs 4: Tabular Data](https://mlsanity.com/paper/2606.08259/) (2026)

## Abstract (arXiv)

Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce the first-of-its-kind algorithm that can be combined with any existing DP mechanisms to generate synthetic relational databases. Our algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors in terms of low-order marginal distributions while maintaining referential integrity. This algorithm eliminates the need to flatten a relational database into a master table (saving space), operates efficiently (saving time), and scales effectively to high-dimensional data. We provide both DP and theoretical utility guarantees for our algorithm. Through numerical experiments on real-world datasets, we demonstrate the effectiveness of our method in preserving fidelity to the original data.