20 citations · 20 across the 2 of their papers we have counts for
4 papers
Pushing the Boundaries of Private, Large-Scale Query Answering
Brendan Avent, Aleksandra Korolova
We address the problem of efficiently and effectively answering large numbers of queries on a sensitive dataset while ensuring differential privacy (DP). We separately analyze this…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Automatic Discovery of Privacy-Utility Pareto Fronts
Brendan Avent, Javier Gonzalez, Tom Diethe +2
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off pr…
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Brendan Avent, Aleksandra Korolova, David Zeber +2
We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model…