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arXiv · 2506.00322

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation

Sofiane Mahiou·Amir Dizche·Reza Nazari·Xinmin Wu·Ralph Abbey·Jorge Silva·Georgi Ganev
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Abstract

We propose dpmm, an open-source library for synthetic data generation with Differentially Private (DP) guarantees. It includes three popular marginal models -- PrivBayes, MST, and AIM -- that achieve superior utility and offer richer functionality compared to alternative implementations. Additionally, we adopt best practices to provide end-to-end DP guarantees and address well-known DP-related vulnerabilities. Our goal is to accommodate a wide audience with easy-to-install, highly customizable, and robust model implementations. Our codebase is available from https://github.com/sassoftware/dpmm.

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Sofiane Mahiou, Amir Dizche, Reza Nazari, Xinmin Wu, Ralph Abbey, Jorge Silva, Georgi Ganev. 2025-05-31. dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation. https://arxiv.org/abs/2506.00322

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