开源差分隐私表格数据生成库，支持三种高效模型。
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
- 集成PrivBayes、MST、AIM三种隐私保护建模方法
- 提供端到端差分隐私保障，抵御常见隐私漏洞
- 易安装、可定制，适合研究与工业应用
我们提出dpmm，一个用于具有差分隐私（DP）保障的合成数据生成的开源库。该库包含三种流行的边缘模型——PrivBayes、MST和AIM——在性能和功能上优于其他实现。此外，我们采用最佳实践以提供端到端的差分隐私保证，并解决已知的差分隐私相关漏洞。目标是为广泛用户提供易于安装、高度可定制且鲁棒的模型实现。代码库可通过https://github.com/sassoftware/dpmm获取。
原文摘要 · Abstract (English)
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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