Privacy preserving association rule mining in vertically partitioned data

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Privacy preserving association rule mining in vertically partitioned data

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Edmonton, Alberta, Canada

POSTER SESSION: Poster papers table of contents

Pages: 639 - 644

Year of Publication: 2002

ISBN:1-58113-567-X

Authors

Jaideep Vaidya	Purdue University, West Lafayette, Indiana
Chris Clifton	Purdue University, West Lafayette, Indiana

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 41, Downloads (12 Months): 270, Citation Count: 94

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ABSTRACT

Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 94

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	Alexandre Evfimievski , Johannes Gehrke , Ramakrishnan Srikant, Limiting privacy breaches in privacy preserving data mining, Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.211-222, June 09-11, 2003, San Diego, California
	Wenliang Du , Zhijun Zhan, Building decision tree classifier on private data, Proceedings of the IEEE international conference on Privacy, security and data mining, p.1-8, December 01, 2002, Maebashi City, Japan
	Stanley R. M. Oliveira , Osmar R. Zaïane, Privacy preserving frequent itemset mining, Proceedings of the IEEE international conference on Privacy, security and data mining, p.43-54, December 01, 2002, Maebashi City, Japan
	Stanley R. M. Oliveira , Osmar R. Zaïane, Foundations for an access control model for privacy preservation in multi-relational association rule mining, Proceedings of the IEEE international conference on Privacy, security and data mining, p.19-26, December 01, 2002, Maebashi City, Japan
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	Murat Kantarcioglu , Jaideep Vaidya, An architecture for privacy-preserving mining of client information, Proceedings of the IEEE international conference on Privacy, security and data mining, p.37-42, December 01, 2002, Maebashi City, Japan
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	Vladimir Estivill-Castro , Chris Clifton, Preface: proceedings of the ICDM 2002 workshop on privacy, security, and data mining, Proceedings of the IEEE international conference on Privacy, security and data mining, p..1, December 01, 2002, Maebashi City, Japan
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