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Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

  • Jun Sakuma
  • Rebecca N. Wright
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5828)

Abstract

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

Keywords

Polynomial Kernel Generalization Error Attribute Selection Private Input Privacy Preserve Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jun Sakuma
    • 1
  • Rebecca N. Wright
    • 2
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Rutgers UniversityPiscatawayUSA

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