ArticleBMC medical genomics2020
Privately computing set-maximal matches in genomic data.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Parallel and private generalized suffix tree construction and query on genomic data.BMC genomic data · 2022Article
- Efficient privacy-preserving variable-length substring match for genome sequence.Algorithms for molecular biology : AMB · 2022Article
- iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching.BMC medical genomics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFinding long matches in deoxyribonucleic acid (DNA) sequences in large aligned genetic sequences is a problem of great interest. A paradigmatic application is the identification of distant relatives via large common subsequences in DNA data. However, because of the sensitive nature of genomic data such computations without security consideration might compromise the privacy of the individuals involved.
methodsThe secret sharing technique enables the computation of matches while respecting the privacy of the inputs of the parties involved. This method requires interaction that depends on the circuit depth needed for the computation.
resultsWe design a new depth-optimized algorithm for computing set-maximal matches between a database of aligned genetic sequences and the DNA of an individual while respecting the privacy of both the database owner and the individual. We then implement and evaluate our protocol.
conclusionsUsing modern cryptographic techniques, difficult genomic computations are performed in a privacy-preserving way. We enrich this research area by proposing a privacy-preserving protocol for set-maximal matches.
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