84 citations · 228 across the 15 of their papers we have counts for
6 papers · 1 filter
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Ryan McKenna, Brett Mullins, Daniel Sheldon +1
We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a se…
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay +1
In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents qu…
A workload-adaptive mechanism for linear queries under local differential privacy
Ryan McKenna, Raj Kumar Maity, Arya Mazumdar +1
We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the w…
Fair Decision Making using Privacy-Protected Data
Satya Kuppam, Ryan Mckenna, David Pujol +3
Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who wi…
Optimizing error of high-dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay +1
Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data,…
Ektelo: A Framework for Defining Differentially-Private Computations
Dan Zhang, Ryan McKenna, Ios Kotsogiannis +4
The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework a…