24 citations · 70 across the 18 of their papers we have counts for
4 papers · 1 filter
KNG: The K-Norm Gradient Mechanism
Matthew Reimherr, Jordan Awan
This paper presents a new mechanism for producing sanitized statistical summaries that achieve \emph{differential privacy}, called the \emph{K-Norm Gradient} Mechanism, or KNG. Thi…
Elliptical Perturbations for Differential Privacy
Matthew Reimherr, Jordan Awan
We study elliptical distributions in locally convex vector spaces, and determine conditions when they can or cannot be used to satisfy differential privacy (DP). A requisite condit…
Differentially Private Inference for Binomial Data
Jordan Awan, Aleksandra Slavkovic
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite s…
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan, Ana Kenney, Matthew Reimherr +1
The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries…