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Privacy-Preserving Data Mining on the Web: Foundations and Techniques
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Author(s): Stanley R.M. Oliveira (Embrapa Informática Agropecuária, Brazil)and Osmar R. Zaïane (University of Alberta, Edmonton, Canada)
Copyright: 2009
Pages: 14
Source title:
Handbook of Research on Text and Web Mining Technologies
Source Author(s)/Editor(s): Min Song (New Jersey Institute of Technology, USA)and Yi-Fang Brook Wu (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59904-990-8.ch028
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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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