Encrypted statistical machine learning: new privacy preserving methods

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Title: Encrypted statistical machine learning: new privacy preserving methods
Authors: Aslett, LJM
Esperança, PM
Holmes, CC
Item Type: Working Paper
Abstract: We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and na\"{i}ve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.
Issue Date: 31-Dec-2015
URI: http://hdl.handle.net/10044/1/51064
Copyright Statement: © 2015 The Authors
Appears in Collections:Epidemiology, Public Health and Primary Care

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