Knockoffs Inference under Privacy Constraints

TL;DR

This paper introduces a differentially private extension of the model-X knockoff framework, enabling privacy-preserving variable selection with controlled false discovery rate in high-dimensional data.

Contribution

It proposes a novel DP-knockoff method that maintains FDR control while ensuring data privacy, addressing a key challenge in privacy-sensitive variable selection.

Findings

DP-knockoff guarantees FDR control under privacy constraints

The method preserves power under certain noise conditions

Effective in both low and high dimensional settings

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. In this paper, 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 (DP-knockoff) method can be effectively utilized to safeguard privacy during variable selection with FDR control in both low and high dimensional settings.