A new scheme on privacy preserving association rule mining

Nan Zhang, Shengquan Wang, Wei Zhao

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations

Abstract

We address the privacy preserving association rule mining problem in a system with one data miner and multiple data providers, each holds one transaction. The literature has tacitly assumed that randomization is the only effective approach to preserve privacy in such circumstances. We challenge this assumption by introducing an algebraic techniques based scheme. Compared to previous approaches, our new scheme can identify association rules more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, Dino Pedreschi
PublisherSpringer Verlag
Pages484-495
Number of pages12
ISBN (Print)3540231080, 9783540231080
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3202
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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