Abstract
Data mining is useful means for discovering valuable patterns, associations, trends, and dependencies in data. Data mining is often required to be performed among a group of sites, where the precondition is that no privacy of any site should be leaked out to other sites. In this paper a distributed privacy-preserving data mining algorithm is proposed. The proposed algorithm is characterized with (1) its ability to preserve the privacy without any coordinator site, and specially its ability to resist the collusion; and (2) its lightweight since only the random number is used for preserving the privacy. Performance analysis and experimental results are provided for demonstrating the effectiveness of the proposed algorithm.
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© 2004 Springer-Verlag Berlin Heidelberg
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Fukasawa, T., Wang, J., Takata, T., Miyazaki, M. (2004). An Effective Distributed Privacy-Preserving Data Mining Algorithm. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_47
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DOI: https://doi.org/10.1007/978-3-540-28651-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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