Modelling a secure support vector machine classifier for private data

Sumana, M and Hareesha, K S (2018) Modelling a secure support vector machine classifier for private data. International Journal of Information and Computer Security, 10 (1). pp. 25-40. ISSN 1744-1765

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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

Item Type: Article
Uncontrolled 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.
Subjects: Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 14 Feb 2018 09:05
Last Modified: 14 Feb 2018 09:05

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