# Gaussian Differential Private Bootstrap by Subsampling

arXiv: https://arxiv.org/abs/2505.01197 · HTML: https://mlsanity.com/paper/2505.01197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 8 of 8)

- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Local Differential Privacy and Its Applications: A Comprehensive Survey](https://mlsanity.com/paper/2008.03686/) (2020)
- [A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis](https://mlsanity.com/paper/2010.05253/) (2020)
- [Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies](https://mlsanity.com/paper/2210.06140/) (2022)
- [Simulation-based, Finite-sample Inference for Privatized Data](https://mlsanity.com/paper/2303.05328/) (2023)
- [Resampling methods for private statistical inference](https://mlsanity.com/paper/2402.07131/) (2024)
- [Privacy Amplification by Subsampling in Time Domain](https://mlsanity.com/paper/2201.04762/) (2022)

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

- [Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions](https://mlsanity.com/paper/2511.01303/) (2025)
- [One-Shot Private Confidence Regions via Resampling](https://mlsanity.com/paper/2610.08460/) (2026)

## Abstract (arXiv)

Bootstrap is a common tool for quantifying uncertainty in data analysis. However, besides additional computational costs in the application of the bootstrap on massive data, a challenging problem in bootstrap based inference under Differential Privacy consists in the fact that it requires repeated access to the data. As a consequence, bootstrap based differentially private inference requires a significant increase of the privacy budget, which on the other hand comes with a substantial loss in statistical accuracy. A potential solution to reconcile the conflicting goals of statistical accuracy and privacy is to analyze the data under parametric model assumptions and in the last decade, several parametric bootstrap methods for inference under privacy have been investigated. However, uncertainty quantification by parametric bootstrap is only valid if the the quantities of interest can be identified as the parameters of a statistical model and the imposed model assumptions are (at least approximately) satisfied. An alternative to parametric methods is the empirical bootstrap that is a widely used tool for non-parametric inference and well studied in the non-private regime. However, under privacy, less insight is available. In this paper, we propose a private empirical m out of n bootstrap and validate its consistency and privacy guarantees under Gaussian Differential Privacy. Compared to the the private n out of n bootstrap, our approach has several advantages. First, it comes with less computational costs, in particular for massive data. Second, the proposed procedure needs less additional noise in the bootstrap iterations, which leads to an improved statistical accuracy while asymptotically guaranteeing the same level of privacy. Third, we demonstrate much better finite sample properties compared to the currently available procedures.