Privacy Preserving Association Rule Mining Revisited : Privacy Enhancement and Resources Efficiency

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概要

• 論文の詳細を見る

• Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (FS). In particular, our analysis shows that the FS scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of FS by fake transactions filtering. In order to overcome this problem, we improve the FS scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.

• （社）電子情報通信学会の論文
• 2010-02-01

著者

• Nyang Daehun

  Inha University

• Hong Dowon

  Software And Contents Research Laboratory Information Security Research Division Cryptography Resear

• MOHAISEN Abedelaziz

  University of Minnesota Twin Cities

• JHO Nam-Su

  Software and Contents Research Laboratory, Information Security Research Division, Cryptography Rese

• Jho Nam-su

  Software And Contents Research Laboratory Information Security Research Division Cryptography Resear

関連論文

• Privacy Preserving Association Rule Mining Revisited : Privacy Enhancement and Resources Efficiency

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