3 papers
stat.ME2026
Setting the Privacy Budget in Differential Privacy by Bounding Adversaries' Odds of Learning Sensitive Information
Ruwimal Y. Pathiraja, Jerome P. Reiter
Differential privacy is a mathematical definition of what it means to protect data subjects' privacy in data releases. Differential privacy depends on a parameter known as the…
stat.ME2024
Bayesian Inference Under Differential Privacy With Bounded Data
Zeki Kazan, Jerome P. Reiter
We describe Bayesian inference for the parameters of Gaussian models of bounded data protected by differential privacy. Using this setting, we demonstrate that analysts can and sho…
cs.CR2024
Differentially Private Verification of Survey-Weighted Estimates
Tong Lin, Jerome P. Reiter
Several official statistics agencies release synthetic data as public use microdata files. In practice, synthetic data do not admit accurate results for every analysis. Thus, it is…