Finding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-03-01 DOI:10.1089/cmb.2023.0050
Magsarjav Bataa, Siwoo Song, Kunsoo Park, Miran Kim, Jung Hee Cheon, Sun Kim
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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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通过同态加密寻找基因组序列的高度相似区。
寻找基因组序列的高度相似区域是基因组分析的一项基本计算。在云环境中可以高效处理大量数据的基因组分析,但将其外包到云中会引发对隐私和安全问题的担忧。同态加密(HE)是一种功能强大的加密原语,可以保护在不受信任的云环境中处理的各种分析中基因组数据的隐私。我们介绍了一种高效算法,用于寻找两个同态加密序列的高度相似区域,并介绍了如何使用比特和字的HE方案来实现该算法。在实验中,我们的算法在耗时方面比现有算法高出两个数量级。总之,它能在可行的时间内找到真实数据集中高度相似的序列区域。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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