431 citations · 727 across the 10 of their papers we have counts for
7 papers · 1 filter
SEALion: a Framework for Neural Network Inference on Encrypted Data
Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law
We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption. It allows one to learn deep neural networks that can be seamlessly…
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law +4
Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, name…
Tsallis Regularized Optimal Transport and Ecological Inference
Boris Muzellec, Richard Nock, Giorgio Patrini +1
Optimal transport is a powerful framework for computing distances between probability distributions. We unify the two main approaches to optimal transport, namely Monge-Kantorovitc…
The Crossover Process: Learnability and Data Protection from Inference Attacks
Richard Nock, Giorgio Patrini, Finnian Lattimore +1
It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack ---…
Fast Learning from Distributed Datasets without Entity Matching
Giorgio Patrini, Richard Nock, Stephen Hardy +1
Consider the following data fusion scenario: two datasets/peers contain the same real-world entities described using partially shared features, e.g. banking and insurance company r…
Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock +1
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by…