Privacy-preserving mining of association rules from outsourced transaction databases

Fosca Giannotti, Laks V.S. Lakshmanan, Anna Monreale, Dino Pedreschi, Hui Wang

Research output: Contribution to journalArticlepeer-review

105 Scopus citations

Abstract

Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-{1} other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.

Original languageEnglish
Article number6365738
Pages (from-to)385-395
Number of pages11
JournalIEEE Systems Journal
Volume7
Issue number3
DOIs
StatePublished - 2013

Keywords

  • Association rule mining
  • privacy-preserving outsourcing

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