Toward Standardization in Privacy-Preserving Data Mining

Stanley R. M. Oliveira, Department of Computing Science, University of Alberta, Edmonton, Canada, and Osmar R. Zaïane, Department of Computing Science, University of Alberta, Edmonton, Canada

3/21/2008
(Original Publish Date: 1/2/2004)

Issues about privacy-preserving data mining (PPDM) have emerged globally. The recent proliferation in PPDM techniques is evident. Motivated by the increasing number of successful techniques, the new generation in PPDM moves on toward standardization because it will certainly play an important role in the future of PPDM. In this paper, we lay out what needs to be done and take some steps toward proposing such standardization: First, we describe the problems we face in defining what information is private in data mining, and discuss how privacy can be violated in data mining. Then, we define privacy preservation in data mining based on users' personal information and information concerning their collective activity. Second, we analyze the implications of the Organization for Economic Cooperation and Development (OECD) data privacy principles in the context of data mining and suggest some policies for PPDM based on such principles. Finally, we propose some requirements to guide the development and deployment of technical solutions.