# How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

arXiv: https://arxiv.org/abs/2512.03238 · HTML: https://mlsanity.com/paper/2512.03238/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2025.

## Built on by (most cited first, 13 of 13)

- [On the Price of Privacy for Language Identification and Generation](https://mlsanity.com/paper/2604.07238/) (2026)
- [EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors](https://mlsanity.com/paper/2602.21218/) (2026)
- [Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data](https://mlsanity.com/paper/2602.02766/) (2026)
- [Minimax optimal differentially private synthetic data for smooth queries](https://mlsanity.com/paper/2602.01607/) (2026)
- [Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation](https://mlsanity.com/paper/2602.03791/) (2026)
- [Privacy Amplification Persists under Unlimited Synthetic Data Release](https://mlsanity.com/paper/2602.04895/) (2026)
- [Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation](https://mlsanity.com/paper/2602.09288/) (2026)
- [MAPLE: Metadata Augmented Private Language Evolution](https://mlsanity.com/paper/2603.19258/) (2026)
- [Advancing the State-of-the-Art in Empirical Privacy Auditing](https://mlsanity.com/paper/2606.10481/) (2026)
- [Fixed-Parameter Tractability of Private Synthetic Data Generation](https://mlsanity.com/paper/2606.11283/) (2026)
- [Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data](https://mlsanity.com/paper/2606.16952/) (2026)
- [Differentially Private Mixing of Public Datasets Improves Private Learning](https://mlsanity.com/paper/2610.06636/) (2026)
- [Tight End-to-End Accounting for Differentially Private Synthetic Data](https://mlsanity.com/paper/2610.17249/) (2026)

## Abstract (arXiv)

High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated data will soon have been used. Additionally, publicly available data often is not representative of users of a particular system -- for example, a research speech dataset of contractors interacting with an AI assistant will likely be more homogeneous, well articulated and self-censored than real world commands that end users will issue. Therefore unlocking high-quality data grounded in real user interactions is of vital interest. However, the direct use of user data comes with significant privacy risks. Differential Privacy (DP) is a well established framework for reasoning about and limiting information leakage, and is a gold standard for protecting user privacy. The focus of this work, Differentially Private Synthetic data, refers to synthetic data that preserves the overall trends of source data,, while providing strong privacy guarantees to individuals that contributed to the source dataset. DP synthetic data can unlock the value of datasets that have previously been inaccessible due to privacy concerns and can replace the use of sensitive datasets that previously have only had rudimentary protections like ad-hoc rule-based anonymization. In this paper we explore the full suite of techniques surrounding DP synthetic data, the types of privacy protections they offer and the state-of-the-art for various modalities (image, tabular, text and decentralized). We outline all the components needed in a system that generates DP synthetic data, from sensitive data handling and preparation, to tracking the use and empirical privacy testing. We hope that work will result in increased adoption of DP synthetic data, spur additional research and increase trust in DP synthetic data approaches.