Encrypted statistical machine learning: new privacy preserving methods

2015/08/27 by Louis J. M. Aslett, Aslett, Louis J. M., Pedro M. Esperança +3
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.1508.06845

openalex publication_date 2015/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and naïve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.

Citations

• A review of homomorphic encryption and software tools for encrypted statistical machine learning
• Idiot's Bayes—Not So Stupid After All?

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