24 citations · 70 across the 18 of their papers we have counts for
4 papers · 1 filter
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 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…
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…
Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
Jordan Awan, Aleksandra Slavkovic
Differential privacy (DP), provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data.…