Title: Modelling a secure support vector machine classifier for private data

Authors: M. Sumana; K.S. Hareesha

Addresses: Department of Information Science and Engineering, MSRIT, Bangalore, Karnataka, 560054, India ' Department of Computer Applications, Manipal Institute of Technology, Manipal , Karnataka, 576104, India

Abstract: Privacy preserving data mining engrosses in drawing out information from distributed data without disclosing sensitive information to collaborating sites. This paper aims on the construction of a vertically distributed privacy preserving support vector machine classifier. The learning model is build for datasets, where one of the collaborating parties comprises the dependent attribute. Furthermore, the amount of privacy, computation speed and the accuracy of our classifier outperform other benchmark algorithms. Privacy of the perceptive attributes values of the cooperating sites are retained while performing secure computations. Collaborative classification is performed using these attributes. The site with the dependent attribute is the master site that initiates the process of secure computation to identify support vectors. Homomorphic property is used to protectively compute the data matrix on records/tuples available at sites. The recommended nonlinear privacy preserving classifier provides an accuracy equivalent to the non-privacy undistributed SVM classifier which uses all the attributes directly.

Keywords: support vector machine classification; homomorphic encryption; vertically partitioned data; secure multiparty computation; privacy preserving data mining; PPDM; homomorphic addition; homomorphic multiplication; kernel function; computation cost; accuracy; receiver operating characteristics; Paillier cryptosystem.

DOI: 10.1504/IJICS.2018.089587

International Journal of Information and Computer Security, 2018 Vol.10 No.1, pp.25 - 40

Received: 05 Apr 2016
Accepted: 09 Nov 2016

Published online: 31 Jan 2018 *

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