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

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.