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

TL;DR

This paper reviews homomorphic encryption techniques enabling secure statistical and machine learning computations on encrypted data, discusses current limitations, and presents an R package implementation for practical use.

Contribution

It provides an accessible review of homomorphic encryption schemes for statisticians, highlighting limitations and showcasing a high-performance R package implementation.

Findings

Limited types of algorithms are currently feasible with homomorphic encryption.

Successful applications of homomorphic encryption in statistical and machine learning tasks.

An R package implementing a recent scheme demonstrates practical usability.

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.