SEALion: a Framework for Neural Network Inference on Encrypted Data

no code implementations • 29 Apr 2019 • Tim van Elsloo, Giorgio Patrini, Hamish Ivey-Law

We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption.

Privacy Preserving Transfer Learning

Entity Resolution and Federated Learning get a Federated Resolution

no code implementations • 11 Mar 2018 • Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne

In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains.

Entity Resolution Federated Learning +1

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

no code implementations • 29 Nov 2017 • Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne

Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.

Entity Resolution Federated Learning +1