How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Ponomareva, Natalia Xu, Zheng McMahan, H. Brendan Kairouz, Peter Rosenblatt, Lucas Cohen-Addad, Vincent Guzmán, Cristóbal McKenna, Ryan Andrew, Galen Bie, Alex Yu, Da Kurakin, Alex Zadimoghaddam, Morteza Vassilvitskii, Sergei Terzis, Andreas |
| author_facet | Ponomareva, Natalia Xu, Zheng McMahan, H. Brendan Kairouz, Peter Rosenblatt, Lucas Cohen-Addad, Vincent Guzmán, Cristóbal McKenna, Ryan Andrew, Galen Bie, Alex Yu, Da Kurakin, Alex Zadimoghaddam, Morteza Vassilvitskii, Sergei Terzis, Andreas |
| contents | 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, \emph{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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Ponomareva, Natalia Xu, Zheng McMahan, H. Brendan Kairouz, Peter Rosenblatt, Lucas Cohen-Addad, Vincent Guzmán, Cristóbal McKenna, Ryan Andrew, Galen Bie, Alex Yu, Da Kurakin, Alex Zadimoghaddam, Morteza Vassilvitskii, Sergei Terzis, Andreas Cryptography and Security Artificial Intelligence Machine Learning 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, \emph{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. |
| title | How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.03238 |