mlsanityData to 7 Oct 2026

Graphical-model based estimation and inference for differential privacy

arXiv 1901.09136 ↗First posted 26 Jan 2019cs.LG cs.CR stat.MLcited by 73

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Abstract (arXiv)

Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 20; 2024: 13; 2025: 14; 2026: 14.

73 papers.

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  1. 2021Iterative Methods for Private Synthetic Data: Unifying Framework and New Methodscited by 58
  2. 2022TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Datacited by 56
  3. 2021Differentially Private Query Release Through Adaptive Projectioncited by 50
  4. 2023SoK: Privacy-Preserving Data Synthesiscited by 37
  5. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  6. 2021Differentially Private Quantilescited by 22
  7. 2021Hermite Polynomial Features for Private Data Generationcited by 21
  8. 2023Generating Private Synthetic Data with Genetic Algorithmscited by 18
  9. 2023Private GANs, Revisitedcited by 17
  10. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  11. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  12. 2023DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generationcited by 13
38 more
  1. 2022Evaluating the Fairness Impact of Differentially Private Synthetic Datacited by 9
  2. 2022Noise-Aware Statistical Inference with Differentially Private Synthetic Datacited by 9
  3. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  4. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  5. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  6. 2024Privacy-preserving data release leveraging optimal transport and particle gradient descentcited by 8
  7. 2024Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Datacited by 8
  8. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  9. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 8
  10. 2023Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithmscited by 7
  11. 2023FLAIM: AIM-based Synthetic Data Generation in the Federated Settingcited by 6
  12. 2021Relaxed Marginal Consistency for Differentially Private Query Answeringcited by 5
  13. 2023Privately generating tabular data using language modelscited by 5
  14. 2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
  15. 2023A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacycited by 4
  16. 2024Private Regression via Data-Dependent Sufficient Statistic Perturbationcited by 4
  17. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 4
  18. 2024Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Datacited by 3
  19. 2025Graph-Conditional Flow Matching for Relational Data Generationcited by 3
  20. 2022Private Synthetic Data with Hierarchical Structurecited by 2
  21. 2023ResidualPlanner+: a scalable matrix mechanism for marginals and beyondcited by 2
  22. 2024A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasetscited by 2
  23. 2024Differentially Private Synthetic Data Generation for Relational Databasescited by 2
  24. 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Trainingcited by 2
  25. 2025P2NIA: Privacy-Preserving Non-Iterative Auditingcited by 2
  26. 2025Leveraging Vertical Public-Private Split for Improved Synthetic Data Generationcited by 2
  27. 2025dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generationcited by 2
  28. 2025Privacy Amplification Through Synthetic Data: Insights from Linear Regressioncited by 2
  29. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  30. 2023Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Datacited by 1
  31. 2023Private Synthetic Data Meets Ensemble Learningcited by 1
  32. 2024KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generationcited by 1
  33. 2024Efficient and Private Marginal Reconstruction with Local Non-Negativitycited by 1
  34. 2025Quantitative Auditing of AI Fairness with Differentially Private Synthetic Datacited by 1
  35. 2025Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansioncited by 1
  36. 2025PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structurecited by 1
  37. 2025PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learningcited by 1
  38. 2026Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Datacited by 1

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