Browse by author
Lookup NU author(s): Dr Kanapathippillai Cumanan
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Rahulamathavan Y, Phan R, Veluru S, Cumanan K, Rajarajan M
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Dependable and Secure Computing
Year: 2013
Issue: 99
Pages: 1-1
Print publication date: 12/12/2013
ISSN (print): 1545-5971
ISSN (electronic): 1941-0018
Publisher: Institute of Electrical and Electronics Engineers
URL: http://dx.doi.org/10.1109/TDSC.2013.51
DOI: 10.1109/TDSC.2013.51
Altmetrics provided by Altmetric
