8 citations · 21 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
Differentially Private Hierarchical Count-of-Counts Histograms
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer +2
We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input ta…