• DocumentCode
    3006232
  • Title

    Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity

  • Author

    Beck, M. ; Kerschbaum, Florian

  • Author_Institution
    Inst. of Syst. Archit., Tech. Univ. Dresden, Dresden, Germany
  • fYear
    2013
  • fDate
    June 27 2013-July 2 2013
  • Firstpage
    31
  • Lastpage
    37
  • Abstract
    Consider two parties who want to compare their strings, e.g., genomes, but do not want to reveal them to each other. We present a system for privacy-preserving matching of strings, which differs from existing systems by providing a deterministic approximation instead of an exact distance. It is efficient (linear complexity), non-interactive and does not involve a third party which makes it particularly suitable for cloud computing. We extend our protocol, such that it only reveals whether there is a match and not the exact distance. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms.
  • Keywords
    approximation theory; biology computing; cloud computing; computational complexity; data privacy; genomics; string matching; approximate two-party privacy-preserving string matching; cloud computing; deterministic approximation; genomic sequences; linear complexity; Approximation algorithms; Approximation methods; Encryption; Genomics; Hamming distance; Protocols; approximate string matching; homomorphic encryption; linear complexity; privacy-preserving string comparison; variable length grams;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2013 IEEE International Congress on
  • Conference_Location
    Santa Clara, CA
  • Print_ISBN
    978-0-7695-5006-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2013.14
  • Filename
    6597116