# Differentially Private Synthetic Data with Private Density Estimation

arXiv: https://arxiv.org/abs/2405.04554 · HTML: https://mlsanity.com/paper/2405.04554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 15 of 15)

- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation](https://mlsanity.com/paper/1705.08475/) (2017)
- [A Universal Law of Robustness via Isoperimetry](https://mlsanity.com/paper/2105.12806/) (2021)
- [Differentially Private Diffusion Models](https://mlsanity.com/paper/2210.09929/) (2022)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Differentially Private Synthetic Data via Foundation Model APIs 1: Images](https://mlsanity.com/paper/2305.15560/) (2023)
- [Kernel Thinning](https://mlsanity.com/paper/2105.05842/) (2021)
- [The Bernstein Mechanism: Function Release under Differential Privacy](https://mlsanity.com/paper/1507.04499/) (2015)
- [Differentially Private Database Release via Kernel Mean Embeddings](https://mlsanity.com/paper/1710.01641/) (2017)
- [Privacy of synthetic data: a statistical framework](https://mlsanity.com/paper/2109.01748/) (2021)
- [Private sampling: a noiseless approach for generating differentially private synthetic data](https://mlsanity.com/paper/2109.14839/) (2021)
- [Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data](https://mlsanity.com/paper/2107.05824/) (2021)
- [Deciding Accuracy of Differential Privacy Schemes](https://mlsanity.com/paper/2011.06404/) (2020)

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

- [Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion](https://mlsanity.com/paper/2502.00245/) (2025)
- [Geometry-Adaptive Mechanisms for Private Synthetic Data](https://mlsanity.com/paper/2609.33363/) (2026)

## Abstract (arXiv)

The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator; this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical tasks.