61 citations · 235 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Private Protocols for U-Statistics in the Local Model and Beyond
James Bell, Aurélien Bellet, Adrià Gascón +1
In this paper, we study the problem of computing -statistics of degree , i.e., quantities that come in the form of averages over pairs of data points, in the local model of d…
Improved Summation from Shuffling
Borja Balle, James Bell, Adria Gascon +1
A protocol by Ishai et al.\ (FOCS 2006) showing how to implement distributed -party summation from secure shuffling has regained relevance in the context of the recently propose…
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner +1
Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-ac…
Differentially Private Summation with Multi-Message Shuffling
Borja Balle, James Bell, Adria Gascon +1
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for -party real summation in the shuffle model of differential privacy with error and $Θ(ε\sqrt{n}…
The Privacy Blanket of the Shuffle Model
Borja Balle, James Bell, Adria Gascon +1
This work studies differential privacy in the context of the recently proposed shuffle model. Unlike in the local model, where the server collecting privatized data from users can…