Helger Lipmaa's publications

Cryptographically Private Support Vector Machines

Sven Laur, Helger Lipmaa and Taneli Mielikäinen. Cryptographically Private Support Vector Machines. In Lyle Ungar, Mark Craven, Dimitrios Gunopulos and Tina Eliassi-Rad, editors, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pages 618--624, Philadelphia, USA, August 20--23, 2006. ACM.

File: [.ps.bz2 (127 KB), .pdf (187 KB)] pdf recommended.


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.

Keywords: Privacy Preserving Data Mining, Kernel Methods.

Comment: Preliminary full version is available from http://eprint.iacr.org/2006/198


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