document: Cryptographically private support vector machines 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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