A review of homomorphic encryption and software tools for encrypted statistical machine learning

Title: A review of homomorphic encryption and software tools for encrypted statistical machine learning
Authors: Aslett, LJM
Esperança, PM
Holmes, CC
Item Type: Working Paper
Abstract: Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.
Issue Date: 31-Dec-2015
URI: http://hdl.handle.net/10044/1/51063
Copyright Statement: © 2015 The Authors
Open Access location: https://arxiv.org/abs/1508.06574
Appears in Collections:Epidemiology, Public Health and Primary Care



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