Privacy-preserving evaluation of generalization error and its application to model and attribute selection

Jun Sakuma, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
Number of pages16
StatePublished - 2009
Event1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
Duration: Nov 2 2009Nov 4 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st Asian Conference on Machine Learning, ACML 2009

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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