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Optimized Two Party Privacy Preserving Association Rule Mining Using Fully Homomorphic Encryption

Kaosar, Md. Golam, Paulet, Russell and Yi, Xun (2011) Optimized Two Party Privacy Preserving Association Rule Mining Using Fully Homomorphic Encryption. In: Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA3PP, Melbourne, Australia, October 24-26, 2011, Proceedings, Part I. Xiang, Yang, Cuzzocrea, Alfredo, Hobbs, Michael and Zhou, Wanlei, eds. Lecture Notes in Computer Science, Vol. 7016 . Springer, Heidelberg, pp. 360-370.

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Abstract

In two party privacy preserving association rule mining, the issue to securely compare two integers is considered as the bottle neck to achieve maximum privacy. Recently proposed fully homomorphic encryption (FHE) scheme by Dijk et.al. can be applied in secure computation. Kaosar, Paulet and Yi have applied it in preserving privacy in two-party association rule mining, but its performance is not very practical due to its huge cyphertext, public key size and complex carry circuit. In this paper we propose some optimizations in applying Dijk et.al.’s encryption system to securely compare two numbers. We also applied this optimized solution in preserving privacy in association rule mining (ARM) in two-party settings. We have further enhanced the two party secure association rule mining technique proposed by Kaosar et.al. The performance analysis shows that this proposed solution achieves a significant improvement.

Item Type: Book Section
ISBN: 9783642246494 (print) 9783642246500 (online) 0302-9743 (Series ISSN)
Uncontrolled Keywords: ResPubID23617, privacy preserving, ARM algorithm
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0806 Information Systems
SEO Classification > 8903 Information Services
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Depositing User: VUIR
Date Deposited: 18 Jan 2014 22:29
Last Modified: 01 Feb 2014 22:23
URI: http://vuir.vu.edu.au/id/eprint/9600
DOI: https://doi.org/10.1007/978-3-642-24650-0_31
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Citations in Scopus: 1 - View on Scopus

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