8 papers
Public Good Provision under Locally Private Signals
Behrooz Moosavi Ramezanzadeh, Jordan Awan
We study public-good provision when a planner observes agents' preferences only through a fixed local-privacy channel that randomizes each report before it reaches the planner. We…
Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy
Young Hyun Cho, Jordan Awan
Privacy protection and uncertainty quantification are increasingly important in data-driven decision making. Conformal prediction provides finite-sample marginal coverage, but exis…
Near-Optimal Private Tests for Simple and MLR Hypotheses
Yu-Wei Chen, Raghu Pasupathy, Jordan Awan
We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio cond…
Incomplete U-Statistics of Equireplicate Designs: Berry-Esseen Bound and Efficient Construction
Cesare Miglioli, Jordan Awan
U-statistics are a fundamental class of estimators that generalize the sample mean and underpin much of nonparametric statistics. Although extensively studied in both statistics an…
Optimal Survey Design for Private Mean Estimation
Yu-Wei Chen, Raghu Pasupathy, Jordan A. Awan
This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) a…
dapper: Data Augmentation for Private Posterior Estimation in R
Kevin Eng, Jordan A. Awan, Nianqiao Phyllis Ju +2
This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of param…
Formal Privacy Guarantees with Invariant Statistics
Young Hyun Cho, Jordan Awan
Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invarian…
Differentially Private Covariate Balancing Causal Inference
Yuki Ohnishi, Jordan Awan
Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publish…