Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure. Despite the availability of numerous DP tools, there remains a lack of general techniques for conducting statistical inference under DP. We examine a DP bootstrap procedure that releases multiple private bootstrap estimates to infer the sampling distribution and construct confidence intervals (CIs). Our privacy analysis presents new results on the privacy cost of a single DP bootstrap estimate, applicable to any DP mechanisms, and identifies some misapplications of the bootstrap in the existing literature. Using the Gaussian-DP (GDP) framework (Dong et al.,2022), we show that the release of $B$ DP bootstrap estimates from mechanisms satisfying $(\mu/\sqrt{(2-2/\mathrm{e})B})$-GDP asymptotically satisfies $\mu$-GDP as $B$ goes to infinity. Moreover, we use deconvolution with the DP bootstrap estimates to accurately infer the sampling distribution, which is novel in DP. We derive CIs from our density estimate for tasks such as population mean estimation, logistic regression, and quantile regression, and we compare them to existing methods using simulations and real-world experiments on 2016 Canada Census data. Our private CIs achieve the nominal coverage level and offer the first approach to private inference for quantile regression

Quantitative Central Limit Theorems for Discrete Stochastic Processes

In this paper, we establish a generalization of the classical Central Limit Theorem for a family of stochastic processes that includes stochastic gradient descent and related gradient-based algorithms. Under certain regularity assumptions, we show that the iterates of these stochastic processes converge to an invariant distribution at a rate of O\lrp{1/\sqrt{k}} where $k$ is the number of steps; this rate is provably tight

The Average Number of Goldbach Representations and Zero-Free Regions of the Riemann Zeta-Function

In this paper, we prove an unconditional form of Fujii's formula for the average number of Goldbach representations and show that the error in this formula is determined by a general zero-free region of the Riemann zeta-function, and vice versa. In particular, we describe the error in the unconditional formula in terms of the remainder in the Prime Number Theorem which connects the error to zero-free regions of the Riemann zeta-function.Comment: 22 pages (content in 20 pages), a student project conducted at SJSU under Kyushu University SENTAN-