A distributed privacy-preserving association rules mining scheme using frequent-pattern tree

Chunhua Su, Kouichi Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently together. Nowadays, data collection is ubiquitous in social and business areas. Many companies and organizations want to do the collaborative association rules mining to get the joint benefits. However, the sensitive information leakage is a problem we have to solve and privacy-preserving techniques are strongly needed. In this paper, we focus on the privacy issue of the association rules mining and propose a secure frequent-pattern tree (FP-tree) based scheme to preserve private information while doing the collaborative association rules mining. We show that our scheme is secure and collusion-resistant for n parties, which means that even if n - 1 dishonest parties collude with a dishonest data miner in an attempt to learn the associations rules between honest respondents and their responses, they will be unable to success.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 4th International Conference, ADMA 2008, Proceedings
Pages170-181
Number of pages12
DOIs
Publication statusPublished - Dec 1 2008
Event4th International Conference on Advanced Data Mining and Applications, ADMA 2008 - Chengdu, China
Duration: Oct 8 2008Oct 10 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5139 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Advanced Data Mining and Applications, ADMA 2008
CountryChina
CityChengdu
Period10/8/0810/10/08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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