# Optimizing Fitness-For-Use of Differentially Private Linear Queries

arXiv: https://arxiv.org/abs/2012.00135 · HTML: https://mlsanity.com/paper/2012.00135/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [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)
- [A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report](https://mlsanity.com/paper/2208.01636/) (2022)
- [Efficient and Private Marginal Reconstruction with Local Non-Negativity](https://mlsanity.com/paper/2410.01091/) (2024)
- [Accurate and Scalable Matrix Mechanisms via Divide and Conquer](https://mlsanity.com/paper/2604.00868/) (2026)
- [Differentially Private Range Queries with Correlated Input Perturbation](https://mlsanity.com/paper/2402.07066/) (2024)

## Abstract (arXiv)

In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In this paper, we consider the problem of answering linear queries under differential privacy subject to per-query accuracy constraints. Existing practical frameworks like the matrix mechanism do not provide such fine-grained control (they optimize total error, which allows some query answers to be more accurate than necessary, at the expense of other queries that become no longer useful). Thus, we design a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers. The covariance structure of the noise is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.