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Recent advances in cryptography promise to enable secure statisticalcomputation on encrypted data, whereby a limited set of operations can becarried out without the need to first decrypt. We review these homomorphicencryption schemes in a manner accessible to statisticians and machinelearners, focusing on pertinent limitations inherent in the current state ofthe art. These limitations restrict the kind of statistics and machine learningalgorithms which can be implemented and we review those which have beensuccessfully applied in the literature. Finally, we document a high performanceR package implementing a recent homomorphic scheme in a general framework.

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