Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption

TL;DR

This paper presents novel algorithms for secure matrix multiplication on encrypted data using homomorphic encryption, significantly reducing computational costs and enhancing privacy for cloud-based applications.

Contribution

It introduces a new element-wise algorithm and two HEGMM algorithms that lower the computational cost of homomorphic encryption-based matrix multiplication.

Findings

Algorithms outperform existing HE-based matrix multiplication methods

Significant reduction in computational cost achieved

Enhanced privacy-preserving computation on cloud platforms

Abstract

Despite the cloud enormous technical and financial advantages, security and privacy have always been the primary concern for adopting cloud computing facility, especially for government agencies and commercial sectors with high-security requirements. Homomorphic Encryption (HE) has recently emerged as an effective tool in assuring privacy and security for sensitive applications by allowing computing on encrypted data. One major obstacle to employing HE-based computation, however, is its excessive computational cost, which is multiple magnitudes higher than its counterpart based on the plaintext. In this paper, we study the problem of how to reduce the HE-based computational cost for general Matrix Multiplication (MM), i.e., a fundamental building block for numerous practical applications, by taking advantage of the Single Instruction Multiple Data (SIMD) operation supported by HE schemes. Specifically, we develop a novel element-wise algorithm for general matrix multiplication, based on which we propose two HE-based General Matrix Multiplication (HEGMM) algorithms to reduce the HE computation cost. Our experimental results show that our algorithms can significantly outperform the state-of-the-art approaches of HE-based matrix multiplication.