Collaborative privacy preserving frequent item set mining in vertically partitioned databases

Ehud Gudes, Boris Rozenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Privacy concerns have become an important issue in Data Mining. This paper deal with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each individual database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent item sets and analyze their security, privacy and complexity properties.

Original languageEnglish
Title of host publicationData and Applications Security XVII
Subtitle of host publicationStatus and Prospects - IFIP TC11 / WG11.3 17th Annual Working Conference on Data and Applications Security
PublisherSpringer New York LLC
Pages91-104
Number of pages14
ISBN (Print)1402080697, 9781402080692
StatePublished - 1 Jan 2004
EventIFIP TC11 / WG11.3 17th Annual Working Conference on Data and Applications Security - Estes Park, CO, United States
Duration: 4 Aug 20036 Aug 2003

Publication series

NameIFIP Advances in Information and Communication Technology
Volume142
ISSN (Print)1868-4238

Conference

ConferenceIFIP TC11 / WG11.3 17th Annual Working Conference on Data and Applications Security
Country/TerritoryUnited States
CityEstes Park, CO
Period4/08/036/08/03

Keywords

  • Association rule
  • Item set
  • Privacy

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

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