24 citations · 70 across the 18 of their papers we have counts for
20 papers
Optimizing Noise for -Differential Privacy via Anti-Concentration and Stochastic Dominance
Jordan Awan, Aishwarya Ramasethu
In this paper, we establish anti-concentration inequalities for additive noise mechanisms which achieve -differential privacy (-DP), a notion of privacy phrased in terms of a…
Differentially Private Topological Data Analysis
Taegyu Kang, Sehwan Kim, Jinwon Sohn +1
This paper is the first to attempt differentially private (DP) topological data analysis (TDA), producing near-optimal private persistence diagrams. We analyze the sensitivity of p…
Simulation-based, Finite-sample Inference for Privatized Data
Jordan Awan, Zhanyu Wang
Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions…
Locally Private Causal Inference for Randomized Experiments
Yuki Ohnishi, Jordan Awan
Local differential privacy is a differential privacy paradigm in which individuals first apply a privacy mechanism to their data (often by adding noise) before transmitting the res…
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…