3 papers
cs.CR2024
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Rachel Cummings, Shlomi Hod, Jayshree Sarathy +1
Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the…
cs.LG2022
Differentially Private Sampling from Distributions
Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith +1
We initiate an investigation of private sampling from distributions. Given a dataset with independent observations from an unknown distribution , a sampling algorithm must o…
cs.CR2019
Improved Differentially Private Analysis of Variance
Marika Swanberg, Ira Globus-Harris, Iris Griffith +3
Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is…