Privately computing set-maximal matches in genomic data
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12920_2020_Article_718.pdf
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1.59 MB
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f902876656873e1906ce2c1ccaab32eb
Author(s)
Sotiraki, Katerina
Ghosh, Esha
Chen, Hao
Date Issued
July 2020
Journal
BMC Medical Genomics
Publisher
Springer Science and Business Media LLC
Citation
Sotiraki, Katerina et al. "Privately computing set-maximal matches in genomic data." BMC Medical Genomics 13, 72 (July 2020): 72 © 2020 Springer Nature
Version
Final published version
Abstract
Background: Finding 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. Methods: The 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. Results: We 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. Conclusions: Using 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s12920-020-0718-x
