A review of homomorphic encryption and software tools for encrypted statistical machine learning
Machine Learning
2015-08-27 v1 Cryptography and Security
Machine Learning
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.
Cite
@article{arxiv.1508.06574,
title = {A review of homomorphic encryption and software tools for encrypted statistical machine learning},
author = {Louis J. M. Aslett and Pedro M. Esperança and Chris C. Holmes},
journal= {arXiv preprint arXiv:1508.06574},
year = {2015}
}
Comments
21 pages, technical report