Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2024

Abstract

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the key generation phase alongside the development of efficient algorithms for matrix-vector multiplication and matrix-matrix multiplication. Comprehensive security evaluations and performance benchmarks demonstrate that OHSS not only fulfills stringent security criteria but also boasts superior efficiency.

Keywords

Homomorphic Encryption, Homomorphic Secret Sharing, Matrix-Matrix Multiplication, Matrix-Vector Multiplication

Discipline

Information Security | Theory and Algorithms

Research Areas

Cybersecurity

Publication

2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom): Sanya, China, December 17-21: Proceedings

Issue

2024

First Page

2213

Last Page

2220

ISBN

9798331506209

Identifier

10.1109/TrustCom63139.2024.00305

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/TrustCom63139.2024.00305

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