mlsanityData to 14 Oct 2026

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

arXiv 2506.00322 ↗First posted 31 May 2025cs.CR cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 31 May 2025.

Builds on

23 of 45 references.

References · 23

  1. 2018Differentially Private Generative Adversarial Networkcited by 235
  2. 2021Adversary Instantiation: Lower Bounds for Differentially Private Machine Learningcited by 199
  3. 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 102
  4. 2022AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Datacited by 90
  5. 2021Benchmarking Differentially Private Synthetic Data Generation Algorithmscited by 81
  6. 2019Graphical-model based estimation and inference for differential privacycited by 74
  7. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 59
  8. 2019G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminatorscited by 53
  9. 2021Differentially Private Query Release Through Adaptive Projectioncited by 52
  10. 2020New Oracle-Efficient Algorithms for Private Synthetic Data Releasecited by 43
  11. 2017Differentially Private Mixture of Generative Neural Networkscited by 39
  12. 2023SoK: Privacy-Preserving Data Synthesiscited by 38
11 more
  1. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  2. 2016PrivTree: A Differentially Private Algorithm for Hierarchical Decompositionscited by 22
  3. 2024"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generationcited by 21
  4. 2024Differentially Private Release of Israel's National Registry of Live Birthscited by 17
  5. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  6. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 13
  7. 2022dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generationcited by 12
  8. 2023Synthetic Data: Methods, Use Cases, and Riskscited by 10
  9. 2022Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix Itcited by 8
  10. 2022Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computerscited by 8
  11. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Tight Auditing of Differential Privacy in MST and AIMcited by 1
  2. 2026PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesiscited by 0
  3. 2026Tight End-to-End Accounting for Differentially Private Synthetic Datacited by 0