Title: Privacy preserving-aware over big data in clouds using GSA and MapReduce framework

Authors: Koneti Sekar; Mokkala Padmavathamma

Addresses: Department of Computer Science and Engineering, S.V. Engineering College for Women, Tirupati, India ' Department of Computer Science, S.V. University, Andhra Pradesh, India

Abstract: This paper proposes a privacy preserving-aware-based approach over Big data in clouds using GSA and MapReduce framework. It consists of two modules such as; MapReduce module and evaluation module. In MR module, convolution process is applied to the dataset and creates a new kernel matrix. The convolution process is correctly done; the utility and privacy information of the data is well secured. Once the convolution process is over, the privacy-persevering framework over big data in cloud systems is performed based on the evaluation module. In Evaluation module, the neural-network is trained based on the Gravitational Search Algorithm with Scaled conjugate gradient (GSA-SCG) algorithm which is improving the utility of the privacy data. Finally, the reduced privacy data's are stored in the service provider (CSP). The MapReduce framework is to ensure the private data, which is in charge for anonymising original data sets as per privacy requirements.

Keywords: MapReduce; privacy preserving; big data; cloud service provider; CSP; cloud system; gravitational search algorithm; GSA; convolution; entropy.

DOI: 10.1504/IJBIDM.2020.104742

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.2, pp.150 - 176

Received: 28 Mar 2017
Accepted: 01 Aug 2017
Published online: 30 Jan 2020 *

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