# Differentially Private Variable Selection via the Knockoff Filter

arXiv: https://arxiv.org/abs/2109.05402 · HTML: https://mlsanity.com/paper/2109.05402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Controlling the false discovery rate via knockoffs](https://mlsanity.com/paper/1404.5609/) (2014)
- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [Deep Knockoffs](https://mlsanity.com/paper/1811.06687/) (2018)
- [Differentially Private Ordinary Least Squares](https://mlsanity.com/paper/1507.02482/) (2015)
- [A knockoff filter for high-dimensional selective inference](https://mlsanity.com/paper/1602.03574/) (2016)
- [Differentially Private False Discovery Rate Control](https://mlsanity.com/paper/1807.04209/) (2018)
- [IPAD: Stable Interpretable Forecasting with Knockoffs Inference](https://mlsanity.com/paper/1809.05032/) (2018)

## Built on by (most cited first, 1 of 1)

- [Variable Selection with the Knockoffs: Composite Null Hypotheses](https://mlsanity.com/paper/2203.02849/) (2022)

## Abstract (arXiv)

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.