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