Accurate Classification Models for Distributed Mining of Privately Preserved Data

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Author(s): Sumana M. (M. S. Ramaiah Institute of Technology, Bangalore, India)and Hareesha K. S. (Manipal Institute of Technology, Udupi, India)
Copyright: 2019
Pages: 17
Source title: Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8897-9.ch022

Keywords: Business & Management / Cyber Ethics / Information Science Reference / IT Security and Ethics

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Abstract

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.

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Accurate Classification Models for Distributed Mining of Privately Preserved Data

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