arXiv:2506.00322
공식 코드 · Papers with Code
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
원문 초록
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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The Prompt is Mightier than the Example
Assessing Generative Models for Structured Data
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
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