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

Yogachandran Rahulamathavan, Raphael C.W. Phan, Suresh Veluru, Kanapathippillai Cumanan, Muttukrishnan Rajarajan

Research output: Contribution to journalArticleResearchpeer-review

81 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)467-479
Number of pages13
JournalIEEE Transactions on Dependable and Secure Computing
Issue number5
Publication statusPublished - 1 Sep 2014
Externally publishedYes


  • cloud computing
  • data classification
  • homomorphic encryption
  • Privacy
  • support vector machine

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