Protecting privacy in incremental maintenance for distributed association rule mining

W. K. Wong, David W. Cheung, Edward Hung, Huan Liu

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

2 Scopus citations

Abstract

Distributed association rule mining algorithms are used to discover important knowledge from databases. Privacy concerns can prevent parties from sharing the data. New algorithms are required to solve traditional mining problems without disclosing (original or derived) information of their own data to other parties. Research results have been developed on (i) incrementally maintaining the discovered association rules, and (ii) computing the distributed association rules while preserving privacy. However, no study has been conducted on the problem of the maintenance of the discovered rules with privacy protection when new sites join the old sites. We propose an algorithm SIMDAR for this problem. Some techniques we developed can even further reduce the cost in a normal association rule mining algorithm with privacy protection. Experimental results showed that SIMDAR can significantly reduce the workload at the old sites by up to 80%.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Pages381-392
Number of pages12
DOIs
StatePublished - 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: May 20 2008May 23 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
Country/TerritoryJapan
CityOsaka
Period5/20/085/23/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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