431 citations · 538 across the 4 of their papers we have counts for
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cs.LG2019★ 36 cited
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…
cs.LG2017★ 431 cited
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…