dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912405628387328 |
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| author | Mahiou, Sofiane Dizche, Amir Nazari, Reza Wu, Xinmin Abbey, Ralph Silva, Jorge Ganev, Georgi |
| author_facet | Mahiou, Sofiane Dizche, Amir Nazari, Reza Wu, Xinmin Abbey, Ralph Silva, Jorge Ganev, Georgi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation Mahiou, Sofiane Dizche, Amir Nazari, Reza Wu, Xinmin Abbey, Ralph Silva, Jorge Ganev, Georgi Cryptography and Security Artificial Intelligence Machine Learning 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. |
| title | dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.00322 |