Finding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption
- Authors
- Bataa, Magsarjav; Song, Siwoo; Park, Kunsoo; Kim, Miran; Cheon, Jung Hee; Kim, Sun
- Issue Date
- Mar-2024
- Publisher
- Mary Ann Liebert Inc.
- Keywords
- highly similar region; homomorphic encryption; privacy-preserving computation; sequence alignment
- Citation
- Journal of Computational Biology, v.31, no.3, pp 197 - 212
- Pages
- 16
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Computational Biology
- Volume
- 31
- Number
- 3
- Start Page
- 197
- End Page
- 212
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/196953
- DOI
- 10.1089/cmb.2023.0050
- ISSN
- 1066-5277
1557-8666
- Abstract
- Finding 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.
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