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Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud

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journal contribution
posted on 02.02.2017, 11:11 by Yogachandran Rahulamathavan, Raphael C.-W. Phan, Suresh Veluru, Kanapathippillai Cumanan, Muttukrishnan Rajarajan
Emerging cloud computing infrastructure replaces traditional outsourcing techniques and provides flexible services to clients at different locations via Internet. This leads to the requirement for data classification to be performed by potentially untrusted servers in the cloud. Within this context, classifier built by the server can be utilized by clients in order to classify their own data samples over the cloud. In this paper, we study a privacy-preserving (PP) data classification technique where the server is unable to learn any knowledge about clients' input data samples while the server side classifier is also kept secret from the clients during the classification process. More specifically, to the best of our knowledge, we propose the first known client-server data classification protocol using support vector machine. The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation. At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers.

History

School

  • Loughborough University London

Published in

IEEE Transactions on Dependable and Secure Computing

Volume

11

Issue

5

Pages

467 - 479

Citation

RAHULAMATHAVAN, Y. ... et al, 2013. Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud. IEEE Transactions on Dependable and Secure Computing, 11 (5), pp. 467-479.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2013

Notes

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

1545-5971

Language

en

Exports