24 citations · 70 across the 18 of their papers we have counts for
4 papers · 1 filter
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…