# Adaptive False Discovery Rate Control with Privacy Guarantee

arXiv: https://arxiv.org/abs/2305.19482 · HTML: https://mlsanity.com/paper/2305.19482/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Controlling the false discovery rate via knockoffs](https://mlsanity.com/paper/1404.5609/) (2014)
- [Differentially private inference via noisy optimization](https://mlsanity.com/paper/2103.11003/) (2021)
- [AdaPT: An interactive procedure for multiple testing with side information](https://mlsanity.com/paper/1609.06035/) (2016)
- [Differentially Private False Discovery Rate Control](https://mlsanity.com/paper/1807.04209/) (2018)
- [False Discovery Rate Control via Data Splitting](https://mlsanity.com/paper/2002.08542/) (2020)
- [ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls](https://mlsanity.com/paper/1905.11465/) (2019)
- [A unified treatment of multiple testing with prior knowledge using the p-filter](https://mlsanity.com/paper/1703.06222/) (2017)
- [Private False Discovery Rate Control](https://mlsanity.com/paper/1511.03803/) (2015)
- [Optimal Multiple Testing Under a Gaussian Prior on the Effect Sizes](https://mlsanity.com/paper/1504.02935/) (2015)
- [One Step to Efficient Synthetic Data](https://mlsanity.com/paper/2006.02397/) (2020)
- [Asymptotic Distribution-Free Independence Test for High Dimension Data](https://mlsanity.com/paper/2110.07652/) (2021)
- [Accumulation tests for FDR control in ordered hypothesis testing](https://mlsanity.com/paper/1505.07352/) (2015)
- [Robust Model Selection with Application in Single-Cell Multiomics Data](https://mlsanity.com/paper/2305.05714/) (2023)

## Built on by (most cited first, 2 of 2)

- [High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference](https://mlsanity.com/paper/2508.05212/) (2025)
- [Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm](https://mlsanity.com/paper/2401.08150/) (2024)

## Abstract (arXiv)

Differentially private multiple testing procedures can protect the information of individuals used in hypothesis tests while guaranteeing a small fraction of false discoveries. In this paper, we propose a differentially private adaptive FDR control method that can control the classic FDR metric exactly at a user-specified level α with privacy guarantee, which is a non-trivial improvement compared to the differentially private Benjamini-Hochberg method proposed in Dwork et al. (2021). Our analysis is based on two key insights: 1) a novel p-value transformation that preserves both privacy and the mirror conservative property, and 2) a mirror peeling algorithm that allows the construction of the filtration and application of the optimal stopping technique. Numerical studies demonstrate that the proposed DP-AdaPT performs better compared to the existing differentially private FDR control methods. Compared to the non-private AdaPT, it incurs a small accuracy loss but significantly reduces the computation cost.