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
Link To Document :
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