TY - GEN
T1 - Privacy-preserving evaluation of generalization error and its application to model and attribute selection
AU - Sakuma, Jun
AU - Wright, Rebecca N.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70549106962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70549106962&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-05224-8_26
DO - 10.1007/978-3-642-05224-8_26
M3 - Conference contribution
AN - SCOPUS:70549106962
SN - 3642052231
SN - 9783642052231
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 338
EP - 353
BT - Advances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
T2 - 1st Asian Conference on Machine Learning, ACML 2009
Y2 - 2 November 2009 through 4 November 2009
ER -