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Finding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption

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dc.contributor.authorBataa, Magsarjav-
dc.contributor.authorSong, Siwoo-
dc.contributor.authorPark, Kunsoo-
dc.contributor.authorKim, Miran-
dc.contributor.authorCheon, Jung Hee-
dc.contributor.authorKim, Sun-
dc.date.accessioned2024-11-28T14:31:30Z-
dc.date.available2024-11-28T14:31:30Z-
dc.date.issued2024-03-
dc.identifier.issn1066-5277-
dc.identifier.issn1557-8666-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/196953-
dc.description.abstractFinding highly similar regions of genomic sequences is a basic computation of genomic analysis. Genomic analyses on a large amount of data are efficiently processed in cloud environments, but outsourcing them to a cloud raises concerns over the privacy and security issues. Homomorphic encryption (HE) is a powerful cryptographic primitive that preserves privacy of genomic data in various analyses processed in an untrusted cloud environment. We introduce an efficient algorithm for finding highly similar regions of two homomorphically encrypted sequences, and describe how to implement it using the bit-wise and word-wise HE schemes. In the experiment, our algorithm outperforms an existing algorithm by up to two orders of magnitude in terms of elapsed time. Overall, it finds highly similar regions of the sequences in real data sets in a feasible time.-
dc.format.extent16-
dc.language영어-
dc.language.isoENG-
dc.publisherMary Ann Liebert Inc.-
dc.titleFinding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1089/cmb.2023.0050-
dc.identifier.scopusid2-s2.0-85188903946-
dc.identifier.wosid001190818800002-
dc.identifier.bibliographicCitationJournal of Computational Biology, v.31, no.3, pp 197 - 212-
dc.citation.titleJournal of Computational Biology-
dc.citation.volume31-
dc.citation.number3-
dc.citation.startPage197-
dc.citation.endPage212-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusALIGNMENT-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusSEARCH-
dc.subject.keywordAuthorhighly similar region-
dc.subject.keywordAuthorhomomorphic encryption-
dc.subject.keywordAuthorprivacy-preserving computation-
dc.subject.keywordAuthorsequence alignment-
dc.identifier.urlhttps://www.liebertpub.com/doi/10.1089/cmb.2023.0050-
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