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Enhancing the Efficiency in Privacy Preserving Learning of Decision Trees in Partitioned Databases

Lory, Peter (2012) Enhancing the Efficiency in Privacy Preserving Learning of Decision Trees in Partitioned Databases. In: Domingo-Ferrer, Josep and Tinnirello, Ilenia, (eds.) Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2012, Palermo, Italy, September 26-28, 2012. Proceedings. Lecture notes in computer science, 7556. Springer, Berlin, pp. 322-335. ISBN 978-3-642-33626-3.

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Other URL: http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-33626-3


This paper considers a scenario where two parties having private databases wish to cooperate by computing a data mining algorithm on the union of their databases without revealing any unnecessary information. In particular, they want to apply the decision tree learning algorithm ID3 in a privacy preserving manner. Lindell and Pinkas (2002) have presented a protocol for this purpose, which enjoys ...


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Item type:Book section
Date:September 2012
Institutions:Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Alumni or Retired Professors > Professur für Wirtschaftsinformatik & Wirtschaftsmathematik (Prof. Dr. Peter Lory)
Projects:"Regionale Wettbewerbsfähigkeit und Beschäftigung", Bayern, 2007-2013 (EFRE), Teil des SECBIT Projekts
Keywords:Privacy preserving data mining, decision tree learning, twoparty computations, Chebyshev expansion.
Dewey Decimal Classification:000 Computer science, information & general works > 004 Computer science
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Deposited on:04 Oct 2012 06:14
Last modified:04 Oct 2012 06:14
Item ID:25991
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