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

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Privacy in Statistical Databases (PSD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7556))

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Abstract

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 a formal proof of privacy and is considerably more efficient than generic solutions. The crucial point of their protocol is the approximation of the logarithm function by a truncated Taylor series. The present paper improves this approximation by using a suitable Chebyshev expansion. This approach results in a considerably more efficient new version of the protocol.

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Authors and Affiliations

  1. University of Regensburg, D-93040, Regensburg, Germany

    Peter Lory

Authors
  1. Peter Lory
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Editor information

Editors and Affiliations

  1. Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. Dipartimento di Ingegneria Elettrica, Elettronica e delle Telecomunicazioni, di tecnologie Chimiche, Automatica e modelli Matematici, Università degli Studi di Palermo, Viale delle Scienze, edificio 9, 90128, Palermo, Italy

    Ilenia Tinnirello

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Lory, P. (2012). Enhancing the Efficiency in Privacy Preserving Learning of Decision Trees in Partitioned Databases. In: Domingo-Ferrer, J., Tinnirello, I. (eds) Privacy in Statistical Databases. PSD 2012. Lecture Notes in Computer Science, vol 7556. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33627-0_25

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  • DOI: https://doi.org/10.1007/978-3-642-33627-0_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33626-3

  • Online ISBN: 978-3-642-33627-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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