Speed-up of Data Analysis with Kernel Trick in Encrypted Domain

TL;DR

This paper introduces a kernel-based acceleration technique for homomorphic encryption that significantly improves the efficiency of high-dimensional data analysis in secure, encrypted environments, making privacy-preserving machine learning more practical.

Contribution

It presents a novel, HE-agnostic kernel method that reduces computational complexity, especially HE multiplications, enabling faster encrypted data analysis.

Findings

Near constant time complexity for high-dimensional data

Significant reduction in HE multiplication operations

Enhanced accessibility for data scientists in secure analysis

Abstract

Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. However, efficiently processing high-dimensional data in HE, especially for machine learning and statistical (ML/STAT) algorithms, poses a challenge. In this paper, we present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains. This technique, independent of underlying HE mechanisms and complementing existing optimizations, notably reduces costly HE multiplications, offering near constant time complexity relative to data dimension. Aimed at accessibility, this method is tailored for data scientists and developers with limited cryptography background, facilitating advanced data analysis in secure environments.