Knockoff inference under privacy constraints
Authors
Research Topics
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag120 -
Published:
August 06, 2026 -
Added to Tracker:
Aug 07, 2026
Abstract
Abstract Model-X knockoff framework offers a model-free variable selection method that ensures finite-sample false discovery rate (FDR) control. However, the complexity of generating knockoff variables, coupled with the model-free assumption, presents significant challenges for protecting data privacy in this context. We propose a comprehensive framework for knockoff inference within the differential privacy paradigm. Our proposed method guarantees robust privacy protection while preserving the exact FDR control entailed by the original model-X knockoff procedure. We further conduct power analysis and establish sufficient conditions under which the noise added for privacy preservation does not asymptotically compromise power. Through various applications, we demonstrate that the differential privacy knockoff method can be effectively utilized to safeguard privacy during variable selection with FDR control in both low and high dimensional settings.
Author Details
Yingying Fan
AuthorZhanrui Cai
AuthorLan Gao
AuthorResearch Topics & Keywords
Machine Learning
Research AreaCitation Information
APA Format
Yingying Fan
,
Zhanrui Cai
&
Lan Gao
(2026)
.
Knockoff inference under privacy constraints.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag120.
BibTeX Format
@article{paper1502,
title = { Knockoff inference under privacy constraints },
author = {
Yingying Fan
and Zhanrui Cai
and Lan Gao
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag120 },
url = { https://doi.org/10.1093/jrsssb/qkag120 }
}