Cryptographically private support vector machines

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.