Privacy-Preserving Data Mining on the Web: Foundations and Techniques

Privacy-Preserving Data Mining on the Web: Foundations and Techniques

Stanley R.M. Oliveira (Embrapa Informatica Agropecuaria, Brazil) and Osmar R. Zaiane (University of Alberta, Edmonton, Canada)
DOI: 10.4018/978-1-59904-951-9.ch005
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Abstract

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

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