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A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations

Yuling Luo Orcid Logo, Shiqi Zhang Orcid Logo, Shunsheng Zhang Orcid Logo, Junxiu Liu Orcid Logo, Yanhu Wang Orcid Logo, Scott Yang Orcid Logo

ACM Transactions on Embedded Computing Systems, Volume: 22, Issue: 5, Pages: 1 - 22

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1145/3611014

Abstract

Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-...

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Published in: ACM Transactions on Embedded Computing Systems
ISSN: 1539-9087 1558-3465
Published: Association for Computing Machinery (ACM) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa66056
first_indexed 2024-04-15T13:21:47Z
last_indexed 2024-11-25T14:17:25Z
id cronfa66056
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spelling 2024-07-11T14:10:40.1467675 v2 66056 2024-04-15 A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2024-04-15 MACS Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-constrained clients. To solve the problem of large-scale computation, in this article, a secure and efficient framework is proposed to outsource large-scale matrix determinants and linear equations to a cloud. Specifically, the proposed framework contains two protocols, which solve large-scale matrix determinant and linear equations, respectively. In the outsourcing protocols of large-scale matrix determinants and linear equations, the task matrix is encrypted and sent to the cloud by the client. The encrypted task matrix is directly computed by using LU factorization in the cloud. The computed result is returned and verified by the cloud and the client, respectively. The computed result is decrypted if it passes the verification. Otherwise, it is returned to the cloud for recalculation. The framework can protect the input privacy and output privacy of the client. The framework also can guarantee the correctness of the result and reduce the local computational complexity. Furthermore, the experimental results show that the framework can save more than 70% of computing resources after outsourcing computing. Thus, this article provides a secure and efficient alternative for solving large-scale computational tasks. Journal Article ACM Transactions on Embedded Computing Systems 22 5 1 22 Association for Computing Machinery (ACM) 1539-9087 1558-3465 26 9 2023 2023-09-26 10.1145/3611014 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required This research was supported by the National Natural Science Foundation of China under Grants 61801131 and 61976063, Guangxi Natural Science Foundation under Grants 2022GXNSFAA035632 and 2022GXNSFFA035028, research fund of Guangxi Normal University under Grant 2021JC006, the AI+Education research project of Guangxi Humanities Society Science Development Research Center under Grant ZXZJ202205. 2024-07-11T14:10:40.1467675 2024-04-15T11:08:00.8749454 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yuling Luo 0000-0002-0117-4614 1 Shiqi Zhang 0009-0004-3386-1226 2 Shunsheng Zhang 0000-0003-0783-4051 3 Junxiu Liu 0000-0002-9790-1571 4 Yanhu Wang 0000-0002-8318-3038 5 Scott Yang 0000-0002-6618-7483 6
title A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
spellingShingle A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
Scott Yang
title_short A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
title_full A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
title_fullStr A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
title_full_unstemmed A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
title_sort A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Yuling Luo
Shiqi Zhang
Shunsheng Zhang
Junxiu Liu
Yanhu Wang
Scott Yang
format Journal article
container_title ACM Transactions on Embedded Computing Systems
container_volume 22
container_issue 5
container_start_page 1
publishDate 2023
institution Swansea University
issn 1539-9087
1558-3465
doi_str_mv 10.1145/3611014
publisher Association for Computing Machinery (ACM)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-constrained clients. To solve the problem of large-scale computation, in this article, a secure and efficient framework is proposed to outsource large-scale matrix determinants and linear equations to a cloud. Specifically, the proposed framework contains two protocols, which solve large-scale matrix determinant and linear equations, respectively. In the outsourcing protocols of large-scale matrix determinants and linear equations, the task matrix is encrypted and sent to the cloud by the client. The encrypted task matrix is directly computed by using LU factorization in the cloud. The computed result is returned and verified by the cloud and the client, respectively. The computed result is decrypted if it passes the verification. Otherwise, it is returned to the cloud for recalculation. The framework can protect the input privacy and output privacy of the client. The framework also can guarantee the correctness of the result and reduce the local computational complexity. Furthermore, the experimental results show that the framework can save more than 70% of computing resources after outsourcing computing. Thus, this article provides a secure and efficient alternative for solving large-scale computational tasks.
published_date 2023-09-26T09:24:59Z
_version_ 1829003329481474048
score 11.058203

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