Accurate Classification Models for Distributed Mining of Privately Preserved Data

Sumana, M and Hareesha, K S (2016) Accurate Classification Models for Distributed Mining of Privately Preserved Data. International Journal of Information Security and Privacy, 10 (4). pp. 58-60. ISSN 1930-1650

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Data maintained at various sectors, needs to be mined to derive useful inferences. Larger part of the data is sensitive and not to be revealed while mining. Current methods perform privacy preservation classification either by randomizing, perturbing or anonymizing the data during mining. These forms of privacy preserving mining work well for data centralized at a single site. Moreover the amount of information hidden during mining is not sufficient. When perturbation approaches are used, data reconstruction is a major challenge. This paper aims at modeling classifiers for data distributed across various sites with respect to the same instances. The homomorphic and probabilistic property of Paillier is used to perform secure product, mean and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost

Item Type: Article
Uncontrolled Keywords: Back Propagation Training, Homomorphic Property, Naïve Bayesian Classification, Privacy Preserving Mining, Secure Mean, Secure Product, Secure Variance, Vertically Partitioned Data
Subjects: Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 20 Sep 2016 15:11
Last Modified: 20 Sep 2016 15:11

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