A new scheme on privacy preserving association rule mining

Nan Zhang, Shengquan Wang, Wei Zhao

Research output: Contribution to journalArticle

13 Citations (Scopus)

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)
Pages (from-to)484-495
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3202
StatePublished - Dec 1 2004

Fingerprint

Association Rule Mining
Privacy Preserving
Association rules
Miners
Middleware
Private Information
Association Rules
Randomisation
Privacy
Transactions

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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A new scheme on privacy preserving association rule mining. / Zhang, Nan; Wang, Shengquan; Zhao, Wei.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3202, 01.12.2004, p. 484-495.

Research output: Contribution to journalArticle

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