31 citations · 36 across the 5 of their papers we have counts for
9 papers · 1 filter
Private Count Release: A Simple and Scalable Approach for Private Data Analytics
Ryan Rogers
We present a data analytics system that ensures accurate counts can be released with differential privacy and minimal onboarding effort while showing instances that outperform othe…
A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy
Ryan Rogers
There are many existing differentially private algorithms for releasing histograms, i.e. counts with corresponding labels, in various settings. Our focus in this survey is to revis…
Adaptive Privacy Composition for Accuracy-first Mechanisms
Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu +1
In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy. A recent line of work by Li…
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans +21
In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's depl…
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale
Ryan Rogers, Adrian Rivera Cardoso, Koray Mancuhan +5
We describe the privatization method used in reporting labor market insights from LinkedIn's Economic Graph, including the differentially private algorithms used to protect member'…
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics
Mark Cesar, Ryan Rogers
Differential privacy (DP) provides rigorous privacy guarantees on individual's data while also allowing for accurate statistics to be conducted on the overall, sensitive dataset. T…