Cryptology ePrint Archive: Report 2006/198

Cryptographically Private Support Vector Machines

Sven Laur and Helger Lipmaa and Taneli Mielikäinen

Abstract: We study the problem of private classification using kernel methods. More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning algorithms, give private classification protocols and private polynomial kernel computation protocols. The new protocols return their outputs---either the kernel value, the classifier or the classifications---in encrypted form so that they can be decrypted only by a common agreement by the protocol participants. We also show how to use the encrypted classifications to privately estimate many properties of the data and the classifier. The new SVM classifiers are the first to be proven private according to the standard cryptographic definitions.

Category / Keywords: cryptographic protocols / Privacy preserving data mining, kernel methods

Publication Info: The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Date: received 12 Jun 2006, last revised 16 Jun 2006

Contact author: lipmaa at ut ee

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Version: 20060620:075736 (All versions of this report)

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