24 citations · 70 across the 18 of their papers we have counts for
5 papers · 1 filter
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
Zhanyu Wang, Guang Cheng, Jordan Awan
Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure. Despite the availability of numerous…
Differentially Private Kolmogorov-Smirnov-Type Tests
Jordan Awan, Yue Wang
Hypothesis testing is a central problem in statistical analysis, and there is currently a lack of differentially private tests which are both statistically valid and powerful. In t…
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
Jordan Awan, Jinshuo Dong
A canonical noise distribution (CND) is an additive mechanism designed to satisfy -differential privacy (-DP), without any wasted privacy budget. -DP is a hypothesis testi…
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…
Tutte polynomials for regular oriented matroids
Jordan Awan, Olivier Bernardi
The Tutte polynomial is a fundamental invariant of graphs and matroids. In this article, we define a generalization of the Tutte polynomial to oriented graphs and regular oriented…