# Post-processing Private Synthetic Data for Improving Utility on Selected Measures

arXiv: https://arxiv.org/abs/2305.15538 · HTML: https://mlsanity.com/paper/2305.15538/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 58 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 28 of 28)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data](https://mlsanity.com/paper/2201.12677/) (2022)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Graphical-model based estimation and inference for differential privacy](https://mlsanity.com/paper/1901.09136/) (2019)
- [Learning with User-Level Privacy](https://mlsanity.com/paper/2102.11845/) (2021)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data](https://mlsanity.com/paper/2109.11429/) (2021)
- [Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning](https://mlsanity.com/paper/2002.10790/) (2020)
- [Accelerating Stochastic Composition Optimization](https://mlsanity.com/paper/1607.07329/) (2016)
- [New Oracle-Efficient Algorithms for Private Synthetic Data Release](https://mlsanity.com/paper/2007.05453/) (2020)
- [Leveraging Public Data for Practical Private Query Release](https://mlsanity.com/paper/2102.08598/) (2021)
- [Differentially Private Synthetic Data: Applied Evaluations and Enhancements](https://mlsanity.com/paper/2011.05537/) (2020)
- [Private Synthetic Data for Multitask Learning and Marginal Queries](https://mlsanity.com/paper/2209.07400/) (2022)
- [Dual Query: Practical Private Query Release for High Dimensional Data](https://mlsanity.com/paper/1402.1526/) (2014)
- [Kamino: Constraint-Aware Differentially Private Data Synthesis](https://mlsanity.com/paper/2012.15713/) (2020)
- [Private Post-GAN Boosting](https://mlsanity.com/paper/2007.11934/) (2020)
- [Auditing and Generating Synthetic Data with Controllable Trust Trade-offs](https://mlsanity.com/paper/2304.10819/) (2023)
- [Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning](https://mlsanity.com/paper/1912.03250/) (2019)
- [Privacy of synthetic data: a statistical framework](https://mlsanity.com/paper/2109.01748/) (2021)

## Built on by (most cited first, 9 of 9)

- [Privacy-Preserving Instructions for Aligning Large Language Models](https://mlsanity.com/paper/2402.13659/) (2024)
- [Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://mlsanity.com/paper/2508.02853/) (2025)
- [Differentially Private Synthetic Data Generation for Relational Databases](https://mlsanity.com/paper/2405.18670/) (2024)
- [Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training](https://mlsanity.com/paper/2410.19941/) (2024)
- [Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation](https://mlsanity.com/paper/2504.10987/) (2025)
- [Private Synthetic Data Meets Ensemble Learning](https://mlsanity.com/paper/2310.09729/) (2023)
- [Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation](https://mlsanity.com/paper/2602.04785/) (2026)
- [Optimal Post-processing of Synthetic Data for Pearson Correlation Matching](https://mlsanity.com/paper/2510.02405/) (2025)
- [PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis](https://mlsanity.com/paper/2609.39630/) (2026)

## Abstract (arXiv)

Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms.