{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dpmm-differentially-private-marginal-models-a","title":"dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation","arxiv_id":"2506.00322","date":"2025-05-31","proceeding":null,"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.","url_abs":"https://arxiv.org/abs/2506.00322v1","url_pdf":"https://arxiv.org/pdf/2506.00322v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dpmm-differentially-private-marginal-models-a","repo_url":"https://github.com/sassoftware/dpmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"},{"task_slug":"tabular-data-generation","task_name":"Tabular Data Generation"}],"methods":[{"method_slug":"adopt","method_name":"ADOPT"},{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}