Secure and efficient general matrix multiplication on cloud using homomorphic encryption.
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| Title: | Secure and efficient general matrix multiplication on cloud using homomorphic encryption. |
|---|---|
| Authors: | Gao, Yang1 (AUTHOR), Quan, Gang2 (AUTHOR), Homsi, Soamar3 (AUTHOR), Wen, Wujie4 (AUTHOR), Wang, Liqiang1 (AUTHOR) liqiang.wang@ucf.edu |
| Source: | Journal of Supercomputing. Dec2024, Vol. 80 Issue 18, p26394-26434. 41p. |
| Subjects: | SIMD (Computer architecture), Matrix multiplications, Government agencies, Algorithms, Privacy |
| Abstract: | Despite the enormous technical and financial advantages of cloud computing, security and privacy have always been the primary concerns for adopting cloud computing facilities, especially for government agencies and commercial sectors with high-security requirements. Homomorphic encryption (HE) has recently emerged as an effective tool in ensuring 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 can be orders of magnitude 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, i.e., a fundamental building block for numerous practical applications, by taking advantage of the single instruction multiple data operations 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 algorithms to reduce the HE computation cost. Our experimental results show that our algorithms significantly outperform the state-of-the-art approaches of HE-based matrix multiplication. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179711738 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Secure and efficient general matrix multiplication on cloud using homomorphic encryption. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gao%2C+Yang%22">Gao, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Quan%2C+Gang%22">Quan, Gang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Homsi%2C+Soamar%22">Homsi, Soamar</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wen%2C+Wujie%22">Wen, Wujie</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Liqiang%22">Wang, Liqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liqiang.wang@ucf.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Dec2024, Vol. 80 Issue 18, p26394-26434. 41p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22SIMD+%28Computer+architecture%29%22">SIMD (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+multiplications%22">Matrix multiplications</searchLink><br /><searchLink fieldCode="DE" term="%22Government+agencies%22">Government agencies</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Despite the enormous technical and financial advantages of cloud computing, security and privacy have always been the primary concerns for adopting cloud computing facilities, especially for government agencies and commercial sectors with high-security requirements. Homomorphic encryption (HE) has recently emerged as an effective tool in ensuring 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 can be orders of magnitude 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, i.e., a fundamental building block for numerous practical applications, by taking advantage of the single instruction multiple data operations 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 algorithms to reduce the HE computation cost. Our experimental results show that our algorithms significantly outperform the state-of-the-art approaches of HE-based matrix multiplication. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=179711738 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-024-06428-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 41 StartPage: 26394 Subjects: – SubjectFull: SIMD (Computer architecture) Type: general – SubjectFull: Matrix multiplications Type: general – SubjectFull: Government agencies Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Privacy Type: general Titles: – TitleFull: Secure and efficient general matrix multiplication on cloud using homomorphic encryption. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gao, Yang – PersonEntity: Name: NameFull: Quan, Gang – PersonEntity: Name: NameFull: Homsi, Soamar – PersonEntity: Name: NameFull: Wen, Wujie – PersonEntity: Name: NameFull: Wang, Liqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 80 – Type: issue Value: 18 Titles: – TitleFull: Journal of Supercomputing Type: main |
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