Differentially Private Variable Selection via the Knockoff Filter
This work addresses privacy-preserving variable selection for data analysts, but it is incremental as it adapts an existing method to incorporate privacy mechanisms.
The authors tackled the problem of performing variable selection with controlled false discovery rate (FDR) while ensuring differential privacy, by proposing a private version of the knockoff filter using Gaussian and Laplace mechanisms, and demonstrated reasonable statistical power in simulations.
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.