Abstract
Everyday, a huge amount of data are produced by many institutions. In most of the cases these data are stored on centralized servers where usually are analyzed to extract knowledge from them. This knowledge is represented by patterns or tendencies that become valuable assets for decision makers. Data analysis requires high performance computing. This situation has motivated the development of Distributed Data Mining (DDM) architectures. DDM uses different distributed data sources to build a global classifier. Building a global classifier implies that all of the data sources be integrated in a unique global dataset. This means that private data have to be shared by every participant. This situation sometimes represents a data privacy intrusion that is not desired by data owners. This paper describes a DDM application where participants work in an interactive way to built a global classifier for data mining process without need sharing the original data. Results show that the global classifier created of this way offers better performance than doing it individually and avoids data privacy intrusion.
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Jasso-Luna, O., Sosa-Sosa, V., Lopez-Arevalo, I. (2008). Global Classifier for Confidential Data in Distributed Datasets. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_30
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DOI: https://doi.org/10.1007/978-3-540-88636-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
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