Abstract
Recently, data mining with capability of preserving privacy has been an area gaining a lot of researcher attention. In fact, researchers working on inference control in statistical databases have long raised a number of related concerns. In the literature, different approaches have been proposed, including the cryptographically secure multiparty computation, random perturbation, and generalization.
Nowadays, the main data mining tasks include association rule mining, classification, clustering and so on. According to different mining missions, there are different privacy-preserving data mining algorithms.
This chapter is organized as follows. Section 14.1 presents the application for privacy-preserving association rule mining. In Section 14.2 privacy-preserving clustering is discussed. In Section 14.3, we give a scheme to privacy-preserving collaborative data mining. In Section 14.4, we introduce the evaluation of privacy preserving and future work. Finally, the conclusion is presented in Section 14.5.
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Yin, Y., Kaku, I., Tang, J., Zhu, J. (2011). Application for Privacy-preserving Data Mining. In: Data Mining. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84996-338-1_14
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DOI: https://doi.org/10.1007/978-1-84996-338-1_14
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