29 citations · 43 across the 4 of their papers we have counts for
6 papers · 1 filter
PrivEdge: From Local to Distributed Private Training and Prediction
Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi +1
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user…
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}…
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal, Matt J. Kusner, Adrià Gascón +1
Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise…
How to Simulate It in Isabelle: Towards Formal Proof for Secure Multi-Party Computation
David Butler, David Aspinall, Adria Gascon
In cryptography, secure Multi-Party Computation (MPC) protocols allow participants to compute a function jointly while keeping their inputs private. Recent breakthroughs are bringi…