Sensitive Items in Privacy Preserving — Association Rule Mining

K. Duraiswamy () and N. Maheswari ()
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K. Duraiswamy: K.S.R. College of Technology, Tiruchengode-637 209, Tamil Nadu, India
N. Maheswari: P.G. Department of Computer Science, Kongu Arts and Science College, Erode-638 107, Tamil Nadu, India

Journal of Information & Knowledge Management (JIKM), 2008, vol. 07, issue 01, 31-35

Abstract: Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

Keywords: Data-mining; privacy preserving; association rules; sensitive items; minimum support; minimum confidence (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S0219649208001932

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