Related papers: On Privately Estimating a Single Parameter

On Privately Estimating a Single Parameter

URL: http://arxiv.org/abs/2503.17252v1
Date: Fri, 21 Mar 2025 15:57:12 GMT
Title: On Privately Estimating a Single Parameter
Authors: Hilal Asi, John C. Duchi, Kunal Talwar,
Abstract summary: We investigate differentially private estimators for individual parameters within larger parametric models.<n>By leveraging these private certificates, we provide computationally and statistical efficient mechanisms that release private statistics that are, at least in the sample size, essentially unimprovable.<n>We investigate the practicality of the algorithms both in simulated data and in real-world data from the American Community Survey and US Census, highlighting scenarios in which the new procedures are successful and identifying areas for future work.
Score: 47.499748486548484
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose on new local notions of estimand stability, and these notions allow procedures that provide private certificates of their own stability. By leveraging these private certificates, we provide computationally and statistical efficient mechanisms that release private statistics that are, at least asymptotically in the sample size, essentially unimprovable: they achieve instance optimal bounds. Additionally, we investigate the practicality of the algorithms both in simulated data and in real-world data from the American Community Survey and US Census, highlighting scenarios in which the new procedures are successful and identifying areas for future work.

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