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

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

Cryptography and Security 2025-06-03 v1 Artificial Intelligence Machine Learning

Authors: Sofiane Mahiou , Amir Dizche , Reza Nazari , Xinmin Wu , Ralph Abbey , Jorge Silva , Georgi Ganev

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.

Keywords

differential privacy software library compiler optimization

Cite

@article{arxiv.2506.00322,
  title  = {dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation},
  author = {Sofiane Mahiou and Amir Dizche and Reza Nazari and Xinmin Wu and Ralph Abbey and Jorge Silva and Georgi Ganev},
  journal= {arXiv preprint arXiv:2506.00322},
  year   = {2025}
}

Comments

Accepted to the Theory and Practice of Differential Privacy Workshop (TPDP 2025)

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

Abstract

Keywords

Cite

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