On the Soundness and Security of Privacy-Preserving SVM for Outsourcing Data Classification - 2017 PROJECT TITLE : On the Soundness and Security of Privacy-Preserving SVM for Outsourcing Data Classification - 2017 ABSTRACT: Recently, Rahulamathavan et al. (IEEE Transactions on Dependable and Secure Computing, 2014, 11(five): 467-479) propose a privacy preserving scheme for outsourcing SVM classification. Their core contribution is a secure protocol to realize the sign of Pailler encrypted numbers. During this paper, we have a tendency to observe that Rahulamathavan et al.’s protocol can suffer from some soundness and security issues. Then, we tend to propose a replacement theme to securely obtain the encrypted numbers’ sign. Theoretical analysis and experiment results show our proposed theme will not only fix the soundness and security issues, however conjointly achieve higher efficiency. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data - 2017 SADI: A Novel Model to Study the Propagation of Social Worms in Hierarchical Networks - 2017
