Inferring decision trees using the minimum description length principle
Information and Computation
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Techniques for Estimating the Computation and Communication Costs of Distributed Data Mining
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
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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.