431 citations · 727 across the 10 of their papers we have counts for
11 papers
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…
Three Tools for Practical Differential Privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem +1
Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on…
Entity Resolution and Federated Learning get a Federated Resolution
Richard Nock, Stephen Hardy, Wilko Henecka +4
Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem…
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…
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini, Alessandro Rozza, Aditya Menon +2
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss…