Optimizing Fitness-For-Use of Differentially Private Linear Queries

Abstract

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.

Geographic Spines in the 2020 Census Disclosure Avoidance System

“…This objective function aligns more closely with our goal when setting geolevel PLB or precision proportions, which is to ensure that the final query estimates for each geolevel satisfies a certain accuracy criterion. The only paper that we are aware of that takes a similar approach is [14].…”