Work

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

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2506.00322

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

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4414889776

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