Abstract
Privacy and security concerns can prevent sharing of data, derailing data mining projects.Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.
This material is based upon work supported by the National Science Foundation under Grant No. 0312357.
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Vaidya, J., Clifton, C. (2005). Privacy-Preserving Decision Trees over Vertically Partitioned Data. In: Jajodia, S., Wijesekera, D. (eds) Data and Applications Security XIX. DBSec 2005. Lecture Notes in Computer Science, vol 3654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535706_11
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DOI: https://doi.org/10.1007/11535706_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28138-2
Online ISBN: 978-3-540-31937-5
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