# Graphical-model based estimation and inference for differential privacy

arXiv: https://arxiv.org/abs/1901.09136 · HTML: https://mlsanity.com/paper/1901.09136/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

## Built on by (most cited first, 30 of 74)

- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data](https://mlsanity.com/paper/2211.06550/) (2022)
- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [SoK: Privacy-Preserving Data Synthesis](https://mlsanity.com/paper/2307.02106/) (2023)
- [Private Synthetic Data for Multitask Learning and Marginal Queries](https://mlsanity.com/paper/2209.07400/) (2022)
- [Differentially Private Quantiles](https://mlsanity.com/paper/2102.08244/) (2021)
- [Hermite Polynomial Features for Private Data Generation](https://mlsanity.com/paper/2106.05042/) (2021)
- [Generating Private Synthetic Data with Genetic Algorithms](https://mlsanity.com/paper/2306.03257/) (2023)
- [Private GANs, Revisited](https://mlsanity.com/paper/2302.02936/) (2023)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility](https://mlsanity.com/paper/2305.10994/) (2023)
- [DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation](https://mlsanity.com/paper/2307.10430/) (2023)
- [Evaluating the Fairness Impact of Differentially Private Synthetic Data](https://mlsanity.com/paper/2205.04321/) (2022)
- [Noise-Aware Statistical Inference with Differentially Private Synthetic Data](https://mlsanity.com/paper/2205.14485/) (2022)
- [Post-processing Private Synthetic Data for Improving Utility on Selected Measures](https://mlsanity.com/paper/2305.15538/) (2023)
- [Synthetic Tabular Data: Methods, Attacks and Defenses](https://mlsanity.com/paper/2506.06108/) (2025)
- [On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation](https://mlsanity.com/paper/2211.15809/) (2022)
- [Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data](https://mlsanity.com/paper/2310.19250/) (2023)
- [Privacy-preserving data release leveraging optimal transport and particle gradient descent](https://mlsanity.com/paper/2401.17823/) (2024)
- [Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data](https://mlsanity.com/paper/2403.07797/) (2024)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms](https://mlsanity.com/paper/2301.08844/) (2023)
- [Relaxed Marginal Consistency for Differentially Private Query Answering](https://mlsanity.com/paper/2109.06153/) (2021)
- [FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://mlsanity.com/paper/2310.03447/) (2023)
- [Privately generating tabular data using language models](https://mlsanity.com/paper/2306.04803/) (2023)
- [The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data](https://mlsanity.com/paper/2504.06923/) (2025)
- [Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions](https://mlsanity.com/paper/2311.17453/) (2023)
- [A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy](https://mlsanity.com/paper/2312.11712/) (2023)
- [Private Regression via Data-Dependent Sufficient Statistic Perturbation](https://mlsanity.com/paper/2405.15002/) (2024)
- [Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data](https://mlsanity.com/paper/2402.04375/) (2024)

## 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.