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A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations
,
Shiqi Zhang ,
Shunsheng Zhang ,
Junxiu Liu ,
Yanhu Wang ,
Scott Yang
ACM Transactions on Embedded Computing Systems, Volume: 22, Issue: 5, Pages: 1 - 22
Swansea University Author:
Scott Yang
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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-...
| Published in: | ACM Transactions on Embedded Computing Systems |
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| ISSN: | 1539-9087 1558-3465 |
| Published: |
Association for Computing Machinery (ACM)
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66056 |
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| 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-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. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
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. |
| Issue: |
5 |
| Start Page: |
1 |
| End Page: |
22 |