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

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 fromthis https URL.

@article{mahiou2025_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 }
}