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