Encrypted statistical machine learning: new privacy preserving methods

TL;DR

This paper introduces two novel privacy-preserving machine learning methods that operate directly on fully homomorphic encrypted data, enabling secure analysis without exposing sensitive information.

Contribution

The paper develops tailored algorithms for encrypted data classification using extremely random forests and naive Bayes, advancing privacy-preserving machine learning techniques.

Findings

Methods perform competitively on classification datasets

Detailed analysis of computational practicality of FHE methods

New cryptographic estimators for encrypted data learning

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 na\"{i}ve 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.