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Privacy-Preserving Classifier Learning

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Financial Cryptography and Data Security (FC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 5628))

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Abstract

We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database held by a remote server without learning any additional information about the records held in the database. The server does not learn anything about the constructed classifier, not even the user’s choice of feature and class attributes.

Our protocol uses several novel techniques to enable oblivious classifier construction. We evaluate a prototype implementation, and demonstrate that its performance is efficient for practical scenarios.

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Author information

Authors and Affiliations

  1. The University of Texas at Austin, Austin, TX, 78712, USA

    Justin Brickell & Vitaly Shmatikov

Authors
  1. Justin Brickell
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  2. Vitaly Shmatikov
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Editor information

Editors and Affiliations

  1. The Tor Project, Dedham, MA, USA

    Roger Dingledine

  2. Palo Alto Research Center, 3333 Coyote Hill Rd, 94304, Palo Alto, CA, USA

    Philippe Golle

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© 2009 Springer-Verlag Berlin Heidelberg

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Brickell, J., Shmatikov, V. (2009). Privacy-Preserving Classifier Learning. In: Dingledine, R., Golle, P. (eds) Financial Cryptography and Data Security. FC 2009. Lecture Notes in Computer Science, vol 5628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03549-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-03549-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03548-7

  • Online ISBN: 978-3-642-03549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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