Accelerating HE Operations from Key Decomposition Technique
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Miran | - |
dc.contributor.author | Lee, Dongwon | - |
dc.contributor.author | Seo, Jinyeong | - |
dc.contributor.author | Song, Yongsoo | - |
dc.date.accessioned | 2023-11-14T08:23:34Z | - |
dc.date.available | 2023-11-14T08:23:34Z | - |
dc.date.created | 2023-10-16 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192209 | - |
dc.description.abstract | Lattice-based homomorphic encryption (HE) schemes are based on the noisy encryption technique, where plaintexts are masked with some random noise for security. Recent advanced HE schemes rely on a decomposition technique to manage the growth of noise, which involves a conversion of a ciphertext entry into a short vector followed by multiplication with an evaluation key. Prior to this work, the decomposition procedure turns out to be the most time-consuming part, as it requires discrete Fourier transforms (DFTs) over the base ring for efficient polynomial arithmetic. In this paper, an expensive decomposition operation over a large modulus is replaced with relatively cheap operations over a ring of integers with a small bound. Notably, the cost of DFTs is reduced from quadratic to linear with the level of a ciphertext without any extra noise growth. We demonstrate the implication of our approach by applying it to the key-switching procedure. Our experiments show that the new key-switching method achieves a speedup of 1.2–2.3 or 2.1–3.3 times over the previous method, when the dimension of a base ring is 215 or 216, respectively. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Accelerating HE Operations from Key Decomposition Technique | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Miran | - |
dc.identifier.doi | 10.1007/978-3-031-38551-3_3 | - |
dc.identifier.scopusid | 2-s2.0-85173057157 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.14084 LNCS, pp.70 - 92 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 14084 LNCS | - |
dc.citation.startPage | 70 | - |
dc.citation.endPage | 92 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Cryptography | - |
dc.subject.keywordPlus | Security of data | - |
dc.subject.keywordPlus | Discrete Fourier transforms | - |
dc.subject.keywordPlus | Ciphertexts | - |
dc.subject.keywordPlus | Decomposition technique | - |
dc.subject.keywordPlus | Encryption operations | - |
dc.subject.keywordPlus | Encryption technique | - |
dc.subject.keywordPlus | External product | - |
dc.subject.keywordPlus | Gadget decomposition | - |
dc.subject.keywordPlus | Ho-momorphic encryptions | - |
dc.subject.keywordPlus | Homomorphic Encryption Schemes | - |
dc.subject.keywordPlus | Homomorphic-encryptions | - |
dc.subject.keywordPlus | Lattice-based | - |
dc.subject.keywordAuthor | External Product | - |
dc.subject.keywordAuthor | Gadget Decomposition | - |
dc.subject.keywordAuthor | Homomorphic Encryption | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-031-38551-3_3#chapter-info | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.