Privately computing set-maximal matches in genomic data

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.

iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching

“…We conclude more efficient algorithms are needed to tackle this challenging task. A research paper from the Microsoft team that describes their approach is included in this special issue [41].…”

Parallel and private generalized suffix tree construction and query on genomic data

“…Additionally, we compared with a private Burrows-Wheeler Transform method [ 11 ] which takes around 160.85 seconds giving us a 4× speedup. Our secure query method is also faster than Sotiraki et al’s [ 12 ] which needed 60 seconds under the same setting.…”

Blockchain for Genomics: A Systematic Literature Review

“…The conflicting need for genomic data to be both shared and private requires the use of privacy-preserving techniques which allow processing of the data while preserving privacy. There is a plethora of research on the topic of privacypreserving processing of genomic data that propose the use of privacy-enhancing technologies such as Homomorphic Encryption [9,38] and secret sharing [114,134] among others. We refer the reader to the surveys on the topic such as [4] for a more systematic presentation.…”