239 citations · 502 across the 13 of their papers we have counts for
17 papers
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…
pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity
Joshua Snoke, Aleksandra Slavković
We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in…
Differentially Private Uniformly Most Powerful Tests 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…
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.…
Providing Accurate Models across Private Partitioned Data: Secure Maximum Likelihood Estimation
Joshua Snoke, Timothy R. Brick, Aleksandra Slavkovic +1
This paper focuses on the privacy paradigm of providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We address the situation whe…