Differentially Private Variable Selection via the Knockoff Filter
Machine Learning
2022-02-01 v3 Cryptography and Security
Databases
Information Theory
Machine Learning
math.IT
Abstract
The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR). We propose a private version of the knockoff filter by incorporating Gaussian and Laplace mechanisms, and show that variable selection with controlled FDR can be achieved. Simulations demonstrate that our setting has reasonable statistical power.
Keywords
Cite
@article{arxiv.2109.05402,
title = {Differentially Private Variable Selection via the Knockoff Filter},
author = {Mehrdad Pournaderi and Yu Xiang},
journal= {arXiv preprint arXiv:2109.05402},
year = {2022}
}
Comments
Accepted to the 2021 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)