PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Cryptographically Private Support Vector Machines
Sven Laur, Helger Lipmaa and Taneli Mielikäinen
In: KDD 2006, 20-23 Aug 2006, Philadelphia, PA, USA.

Abstract

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 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2133
Deposited By:Taneli Mielikäinen
Deposited On:15 July 2006