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J Biomed Inform. 2018 May;81:41-52. doi: 10.1016/j.jbi.2018.03.003. Epub 2018 Mar 15.

Secure count query on encrypted genomic data.

Author information

1
Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: zahidul@cs.umanitoba.ca.
2
Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: mahdi@cs.umanitoba.ca.
3
Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: sadat@cs.umanitoba.ca.
4
Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: noman@cs.umanitoba.ca.

Abstract

Human genomic information can yield more effective healthcare by guiding medical decisions. Therefore, genomics research is gaining popularity as it can identify potential correlations between a disease and a certain gene, which improves the safety and efficacy of drug treatment and can also develop more effective prevention strategies [1]. To reduce the sampling error and to increase the statistical accuracy of this type of research projects, data from different sources need to be brought together since a single organization does not necessarily possess required amount of data. In this case, data sharing among multiple organizations must satisfy strict policies (for instance, HIPAA and PIPEDA) that have been enforced to regulate privacy-sensitive data sharing. Storage and computation on the shared data can be outsourced to a third party cloud service provider, equipped with enormous storage and computation resources. However, outsourcing data to a third party is associated with a potential risk of privacy violation of the participants, whose genomic sequence or clinical profile is used in these studies. In this article, we propose a method for secure sharing and computation on genomic data in a semi-honest cloud server. In particular, there are two main contributions. Firstly, the proposed method can handle biomedical data containing both genotype and phenotype. Secondly, our proposed index tree scheme reduces the computational overhead significantly for executing secure count query operation. In our proposed method, the confidentiality of shared data is ensured through encryption, while making the entire computation process efficient and scalable for cutting-edge biomedical applications. We evaluated our proposed method in terms of efficiency on a database of Single-Nucleotide Polymorphism (SNP) sequences, and experimental results demonstrate that the execution time for a query of 50 SNPs in a database of 50,000 records is approximately 5 s, where each record contains 500 SNPs. And, it requires 69.7 s to execute the query on the same database that also includes phenotypes.

KEYWORDS:

Cloud computing; Data sharing; Genomic data

PMID:
29550393
DOI:
10.1016/j.jbi.2018.03.003
[Indexed for MEDLINE]
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