Abstract
This paper introduces a novel paradigm of privacy preserving mining for distributed databases. The paradigm includes an agent-based approach for distributed learning of a decision tree to fully analyze data located at several distributed sites without revealing any information at each site. The distributed decision tree approach has been developed from the well-known decision tree algorithm, for the distributed and privacy preserving data mining process. It is performed on the agent based architecture dealing with distributed databases in a collaborative fashion. This approach is very useful to be applied to a variety of domains which require information security and privacy during data mining process.
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Baik, S.W., Bala, J., Rhee, D. (2004). An Agent Based Privacy Preserving Mining for Distributed Databases. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_140
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DOI: https://doi.org/10.1007/978-3-540-30497-5_140
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
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