# Towards Instance-Optimal Private Query Release

arXiv: https://arxiv.org/abs/1811.03763 · HTML: https://mlsanity.com/paper/1811.03763/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

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

- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [Privately Learning Subspaces](https://mlsanity.com/paper/2106.00001/) (2021)
- [Private Query Release via the Johnson-Lindenstrauss Transform](https://mlsanity.com/paper/2208.07410/) (2022)
- [Better Private Linear Regression Through Better Private Feature Selection](https://mlsanity.com/paper/2306.00920/) (2023)
- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Subset-Based Instance Optimality in Private Estimation](https://mlsanity.com/paper/2303.01262/) (2023)
- [Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis](https://mlsanity.com/paper/2412.14396/) (2024)
- [Distribution-Aware Mean Estimation under User-level Local Differential Privacy](https://mlsanity.com/paper/2410.09506/) (2024)
- [Accurate, private, secure, federated U-statistics with higher degree](https://mlsanity.com/paper/2603.01986/) (2026)

## Abstract (arXiv)

We study efficient mechanisms for the query release problem in differential privacy: given a workload of m statistical queries, output approximate answers to the queries while satisfying the constraints of differential privacy. In particular, we are interested in mechanisms that optimally adapt to the given workload. Building on the projection mechanism of Nikolov, Talwar, and Zhang, and using the ideas behind Dudley's chaining inequality, we propose new efficient algorithms for the query release problem, and prove that they achieve optimal sample complexity for the given workload (up to constant factors, in certain parameter regimes) with respect to the class of mechanisms that satisfy concentrated differential privacy. We also give variants of our algorithms that satisfy local differential privacy, and prove that they also achieve optimal sample complexity among all local sequentially interactive private mechanisms.