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Efficient Privacy-Preserving Variable-Length Substring Match for Genome Sequence

Authors Yoshiki Nakagawa, Satsuya Ohata, Kana Shimizu

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Author Details

Yoshiki Nakagawa
  • Department of Computer Science and Engineering, Waseda University, Tokyo, Japan
Satsuya Ohata
  • Digital Garage, Inc., Tokyo, Japan
Kana Shimizu
  • Department of Computer Science and Engineering, Waseda University, Tokyo, Japan
  • National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

Funding

This work is partially supported by JST CREST grant number JPMJCR19F6.
  • Shimizu, Kana: Supported part in MEXT/JSPS KAKENHI Grant Number 19K12209 and 21H04871.

Cite As Get BibTex

Yoshiki Nakagawa, Satsuya Ohata, and Kana Shimizu. Efficient Privacy-Preserving Variable-Length Substring Match for Genome Sequence. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 2:1-2:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.WABI.2021.2

Abstract

Finding a similar substring that commonly appears in query and database sequences is an essential task for genome data analysis. This study proposes a secure two-party variable-length string search protocol based on secret sharing. The unique feature of our protocol is that time, communication, and round complexities are not dependent on the database length N, after the query input. This property brings dramatic performance improvements in search time, since N is usually quite large in an actual genome database, and the same database is repeatedly used for many queries. Our concept hinges on a technique that efficiently applies the compressed full-text index (FOCS 2000) for a secret-sharing scheme. We conducted an experiment using a human genomic sequence with the length of 10 million as the database and a query with the length of 100 and found that the query response time of our protocol was at least three orders of magnitude faster than a well-designed baseline protocol under the realistic computation/network environment.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
  • Security and privacy → Privacy-preserving protocols
  • Theory of computation → Cryptographic protocols
  • Applied computing → Genomics
Keywords
  • Private Genome Sequence Search
  • Secure Multiparty Computation
  • Secret Sharing
  • FM-index
  • Suffix Tree
  • Maximal Exact Match

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Supplementary Materials

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