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

Chunhua Su, Kouichi Sakurai

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルAdvanced Data Mining and Applications - 4th International Conference, ADMA 2008, Proceedings
ページ170-181
ページ数12
DOI
出版ステータス出版済み - 12 1 2008
イベント4th International Conference on Advanced Data Mining and Applications, ADMA 2008 - Chengdu, 中国
継続期間: 10 8 200810 10 2008

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5139 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他4th International Conference on Advanced Data Mining and Applications, ADMA 2008
国/地域中国
CityChengdu
Period10/8/0810/10/08

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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