# Certified private data release for sparse Lipschitz functions

arXiv: https://arxiv.org/abs/2302.09680 · HTML: https://mlsanity.com/paper/2302.09680/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2023.

## Built on by (most cited first, 7 of 7)

- [Privacy-preserving data release leveraging optimal transport and particle gradient descent](https://mlsanity.com/paper/2401.17823/) (2024)
- [Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets](https://mlsanity.com/paper/2305.17148/) (2023)
- [Online Differentially Private Synthetic Data Generation](https://mlsanity.com/paper/2402.08012/) (2024)
- [Private Evolution Converges](https://mlsanity.com/paper/2506.08312/) (2025)
- [Robust Privacy: Inference-Stage Privacy through Certified Robustness](https://mlsanity.com/paper/2601.17360/) (2026)
- [Minimax optimal differentially private synthetic data for smooth queries](https://mlsanity.com/paper/2602.01607/) (2026)
- [Geometry-Adaptive Mechanisms for Private Synthetic Data](https://mlsanity.com/paper/2609.33363/) (2026)

## Abstract (arXiv)

As machine learning has become more relevant for everyday applications, a natural requirement is the protection of the privacy of the training data. When the relevant learning questions are unknown in advance, or hyper-parameter tuning plays a central role, one solution is to release a differentially private synthetic data set that leads to similar conclusions as the original training data. In this work, we introduce an algorithm that enjoys fast rates for the utility loss for sparse Lipschitz queries. Furthermore, we show how to obtain a certificate for the utility loss for a large class of algorithms.