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

2015/08/26 by Louis J. M. Aslett, Aslett, Louis J. M., Pedro M. Esperança +3
Computer Science · #Chaos-based Image/Signal Encryption #Cryptographic Implementations and Security #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1508.06574

openalex publication_date 2015/08/26 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

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.

Citations

• On lattices, learning with errors, random linear codes, and cryptography

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