Privacy-Preserving Data Mining and the Need for Confluence of Research and Practice

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Author(s): Lixin Fu (The University of North Carolina at Greensboro, USA), Hamid Nemati (The University of North Carolina at Greensboro, USA)and Fereidoon Sadri (The University of North Carolina at Greensboro, USA)
Copyright: 2008
Pages: 19
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch144

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

Privacy-preserving data mining (PPDM) refers to data mining techniques developed to protect sensitive data while allowing useful information to be discovered from the data. In this article, we review PPDM and present a broad survey of related issues, techniques, measures, applications, and regulation guidelines. We observe that the rapid pace of change in information technologies available to sustain PPDM has created a gap between theory and practice. We posit that without a clear understanding of the practice, this gap will be widening which, ultimately, will be detrimental to the field. We conclude by proposing a comprehensive research agenda intended to bridge the gap relevant to practice and as a reference basis for the future related legislation activities.

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Privacy-Preserving Data Mining and the Need for Confluence of Research and Practice

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