# Leveraging Public Data for Practical Private Query Release

arXiv: https://arxiv.org/abs/2102.08598 · HTML: https://mlsanity.com/paper/2102.08598/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Concentrated Differential Privacy](https://mlsanity.com/paper/1603.01887/) (2016)
- [The Discrete Gaussian for Differential Privacy](https://mlsanity.com/paper/2004.00010/) (2020)
- [Algorithmic Stability for Adaptive Data Analysis](https://mlsanity.com/paper/1511.02513/) (2015)
- [Fingerprinting Codes and the Price of Approximate Differential Privacy](https://mlsanity.com/paper/1311.3158/) (2013)
- [Private Learning and Sanitization: Pure vs. Approximate Differential Privacy](https://mlsanity.com/paper/1407.2674/) (2014)
- [Privacy-preserving Prediction](https://mlsanity.com/paper/1803.10266/) (2018)
- [New Oracle-Efficient Algorithms for Private Synthetic Data Release](https://mlsanity.com/paper/2007.05453/) (2020)
- [Limits of Private Learning with Access to Public Data](https://mlsanity.com/paper/1910.11519/) (2019)
- [Optimizing error of high-dimensional statistical queries under differential privacy](https://mlsanity.com/paper/1808.03537/) (2018)
- [Private Query Release Assisted by Public Data](https://mlsanity.com/paper/2004.10941/) (2020)
- [Dual Query: Practical Private Query Release for High Dimensional Data](https://mlsanity.com/paper/1402.1526/) (2014)
- [Permute-and-Flip: A new mechanism for differentially private selection](https://mlsanity.com/paper/2010.12603/) (2020)
- [Private Post-GAN Boosting](https://mlsanity.com/paper/2007.11934/) (2020)
- [Learning from Mixtures of Private and Public Populations](https://mlsanity.com/paper/2008.00331/) (2020)
- [Privately Answering Classification Queries in the Agnostic PAC Model](https://mlsanity.com/paper/1907.13553/) (2019)
- [Private Multiplicative Weights Beyond Linear Queries](https://mlsanity.com/paper/1407.1571/) (2014)

## Built on by (most cited first, 30 of 39)

- [Differentially Private Fine-tuning of Language Models](https://mlsanity.com/paper/2110.06500/) (2021)
- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [Public Data-Assisted Mirror Descent for Private Model Training](https://mlsanity.com/paper/2112.00193/) (2021)
- [When Does Differentially Private Learning Not Suffer in High Dimensions?](https://mlsanity.com/paper/2207.00160/) (2022)
- [Why Is Public Pretraining Necessary for Private Model Training?](https://mlsanity.com/paper/2302.09483/) (2023)
- [Private Estimation with Public Data](https://mlsanity.com/paper/2208.07984/) (2022)
- [Selective Pre-training for Private Fine-tuning](https://mlsanity.com/paper/2305.13865/) (2023)
- [Generating Private Synthetic Data with Genetic Algorithms](https://mlsanity.com/paper/2306.03257/) (2023)
- [Private Distribution Learning with Public Data: The View from Sample Compression](https://mlsanity.com/paper/2308.06239/) (2023)
- [InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy](https://mlsanity.com/paper/2507.02974/) (2025)
- [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)
- [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)
- [Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning](https://mlsanity.com/paper/2405.08920/) (2024)
- [Relaxed Marginal Consistency for Differentially Private Query Answering](https://mlsanity.com/paper/2109.06153/) (2021)
- [Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free](https://mlsanity.com/paper/2305.09668/) (2023)
- [Mean Estimation Under Heterogeneous Privacy Demands](https://mlsanity.com/paper/2310.13137/) (2023)
- [Oracle-Efficient Differentially Private Learning with Public Data](https://mlsanity.com/paper/2402.09483/) (2024)
- [Learning-Augmented Private Algorithms for Multiple Quantile Release](https://mlsanity.com/paper/2210.11222/) (2022)
- [On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift](https://mlsanity.com/paper/2312.15551/) (2023)
- [Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?](https://mlsanity.com/paper/2502.06555/) (2025)
- [Mitigating Statistical Bias within Differentially Private Synthetic Data](https://mlsanity.com/paper/2108.10934/) (2021)
- [Can Membership Inferencing be Refuted?](https://mlsanity.com/paper/2303.03648/) (2023)
- [Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling](https://mlsanity.com/paper/2210.12575/) (2022)
- [Credit Attribution and Stable Compression](https://mlsanity.com/paper/2406.15916/) (2024)
- [RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models](https://mlsanity.com/paper/2502.12794/) (2025)
- [Private Synthetic Data with Hierarchical Structure](https://mlsanity.com/paper/2206.05942/) (2022)
- [Answering Private Linear Queries Adaptively using the Common Mechanism](https://mlsanity.com/paper/2212.00135/) (2022)
- [ResidualPlanner+: a scalable matrix mechanism for marginals and beyond](https://mlsanity.com/paper/2305.08175/) (2023)

## Abstract (arXiv)

In many statistical problems, incorporating priors can significantly improve performance. However, the use of prior knowledge in differentially private query release has remained underexplored, despite such priors commonly being available in the form of public datasets, such as previous US Census releases. With the goal of releasing statistics about a private dataset, we present PMW^Pub, which -- unlike existing baselines -- leverages public data drawn from a related distribution as prior information. We provide a theoretical analysis and an empirical evaluation on the American Community Survey (ACS) and ADULT datasets, which shows that our method outperforms state-of-the-art methods. Furthermore, PMW^Pub scales well to high-dimensional data domains, where running many existing methods would be computationally infeasible.