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KmerCrypt: private k-mer search with homomorphic encryption.

Kimonas Provatas1, Ioannis Mouratidis1, Ilias Georgakopoulos-Soares1

  • 1Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Dell Paediatric Research Institute, 1400 Barbara Jordan Blvd, Austin, TX 78723, United States.

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Summary

This study introduces a novel homomorphic encryption protocol for secure genomic data analysis. It allows private k-mer searches on encrypted data stored on untrusted servers, ensuring data privacy.

Keywords:
cloud bioinformaticsgenomic privacyhomomorphic encryptionk-mer searchsecure computation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cryptography

Background:

  • Genomic data analysis requires outsourcing storage and computation to third-party servers.
  • This outsourcing presents significant privacy challenges for sensitive human genomic data.
  • K-mer-based analyses are crucial for various applications but require careful ethical and technical considerations.

Purpose of the Study:

  • To develop a secure protocol for private k-mer searches on encrypted genomic data.
  • To enable clients to store and query their genomic data on untrusted servers without compromising privacy.
  • To provide a practical solution for confidential genomic data analysis in clinical and research settings.

Main Methods:

  • A novel protocol utilizing homomorphic encryption for secure genomic data processing.
  • Client-side encryption of the genome before uploading to an untrusted server.
  • Server-side computation on encrypted data (ciphertext) for k-mer searches.
  • Development of KmerCrypt, a toolkit implementing the private k-mer search protocol.

Main Results:

  • The protocol allows private k-mer searches on fully encrypted genomic data.
  • The server never accesses the client's non-encrypted genome or k-mer query content.
  • Encrypted results are returned to the client for decryption, ensuring data owner control.
  • The system supports exact k-mer searches and position weight matrix searches.

Conclusions:

  • Homomorphic encryption provides a secure framework for outsourcing genomic data storage and analysis.
  • The presented protocol transforms cloud servers into secure computation systems for encrypted client-owned genomic data.
  • KmerCrypt offers an efficient and private solution for querying encrypted genomic datasets, addressing critical privacy concerns.