Privacy preserving association rule mining in vertically partitioned data

Jaideep Vaidya, Chris Clifton

Research output: Contribution to conferencePaperpeer-review

650 Scopus citations

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.

Original languageEnglish (US)
Pages639-644
Number of pages6
DOIs
StatePublished - 2002
Externally publishedYes
EventKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Edmonton, Alta, Canada
Duration: Jul 23 2002Jul 26 2002

Other

OtherKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryCanada
CityEdmonton, Alta
Period7/23/027/26/02

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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