A review of homomorphic encryption and software tools for encrypted statistical machine learning
It addresses the problem of enabling secure machine learning for statisticians and machine learners, but is incremental as it reviews existing methods and tools.
The paper reviews homomorphic encryption schemes and their limitations for secure statistical computation on encrypted data, and documents a high-performance R package implementing a recent scheme.
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.