Secure Similar Patients Query on Encrypted Genomic Data

Mahdi, Safiur Rahman; Aziz, Momin Al; Alhadidi, Dima; Mohammed, Noman

doi:10.1109/jbhi.2018.2881086

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…There are many other works that discuss secure similarity comparisons of genetic data, typically in the context of approximating edit distance [3][4][5][6]. Aziz et al [3] used shingle set intersection as an alternative method of the similarity metric.…”

Section: Related Workmentioning

confidence: 99%

“…Zhu et al [ 6 ] focused on looking at small edit sets (VCFs) from shorter sequences and did not look at whole genome similarity, which is what our approach aims to look at. The method by Mahdi et al [ 4 ] aimed to securely compute the Hamming distance to search for the most similar sequences in a database using prefix tree queries. However, the method used a trusted party to encrypt the data and distributes decryption keys to researchers, but the expectation of the existence of a trusted party is not practical.…”

Section: Introductionmentioning

confidence: 99%

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Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development

Woods¹,

Kramer²,

Xu³

et al. 2023

JMIR Bioinform Biotech

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Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development

Woods¹,

Kramer²,

Xu³

et al. 2023

JMIR Bioinform Biotech

View full text Add to dashboard Cite

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“…The primary aim of data encryption is to secure the genomic data (Mahdi et al 2018). Encryption works by encoding data using a secret private key.…”

Section: Encryptionmentioning

confidence: 99%

Building Infrastructure for African Human Genomic Data Management

Parker

Maslamoney

Meintjes

et al. 2019

Data Science Journal

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Human genomic data are large and complex, and require adequate infrastructure for secure storage and transfer. The NIH and The Wellcome Trust have funded multiple projects on genomic research, including the Human Heredity and Health in Africa (H3Africa) initiative, and data are required to be deposited into the public domain. The European Genome-phenome Archive (EGA) is a repository for sequence and genotype data where the data access is controlled by access committees. Access is determined by a formal application procedure for the purpose of secure storage and distribution, and must be in line with the informed consent of the study participants. H3Africa researchers based in Africa and generating their own data can benefit tremendously from the data sharing capabilities of the internet by using the appropriate technologies. The H3Africa Data Archive is an effort between the H3Africa data generating projects, H3ABioNet and the EGA to store and submit genomic data to public repositories. H3ABioNet maintains the security of the H3Africa Data Archive, ensures ethical security compliance, supports users with data submission and facilitates the data transfer. The goal is to ensure efficient data flow between researchers, the archive and the EGA or other public repositories. To comply with the H3Africa data sharing and release policy, nine months after the data is in secure storage, H3ABioNet converts the data into an XML format ready for submission to EGA. This article describes the infrastructure that has been developed for African human genomic data management.

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