Abstract
Privacy preserving data mining is a novel research direction. The main objective is to develop algorithms for modifying the original data in some way, so that the private information remains private even fter the mining process.
Agrawal and Srikant first proposed a scheme for privacy preserving data mining using random perturbation [1]. Then, Rizvi and Haritsa presented a scheme called MASK to mine associations with secrecy constraints [2]; Du and Zhan proposed an approach to conduct privacy preserving decision tree building [3]. A methodology for hiding knowledge in database was also presented and applied to classification and association rule mining [4]. However, all those approaches are different in their frameworks and processes. Only can they deal with a special data type, a given mining algorithm, and one kind of the attribute of private information.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, P., Tong, Y., Tang, S., Yang, D. (2006). KD3 Scheme for Privacy Preserving Data Mining. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_78
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DOI: https://doi.org/10.1007/11760146_78
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