REVIEW 2 cited by
Differentially Private Hypothesis Testing with the Subsampled and Aggregated Randomized Response Mechanism
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Differentially Private Hypothesis Testing with the Subsampled and Aggregated Randomized Response Mechanism
read the original abstract
Randomized response is one of the oldest and most well-known methods for analyzing confidential data. However, its utility for differentially private hypothesis testing is limited because it cannot achieve high privacy levels and low type I error rates simultaneously. In this article, we show how to overcome this issue with the subsample and aggregate technique. The result is a general-purpose method that can be used for both frequentist and Bayesian testing. {{We illustrate the performance of our proposal in three scenarios: goodness-of-fit testing for linear regression models, nonparametric testing of a location parameter with the Wilcoxon test, and the nonparametric Kruskal-Wallis test.
Forward citations
Cited by 2 Pith papers
-
Differentially private hypothesis testing in survival analysis
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
-
Privately Estimating Monotone Statistics in Polynomial Time
Presents polynomial-time DP algorithms for monotone statistics achieving t-factor sample savings over subsample-and-aggregate with matching query lower bounds and applications to eigenvalue and regression estimation.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.