Title: Privacy preservation using hybrid cloud environment and Map-Reduce for data deduplication

Authors: Rutuja Dadasaheb Mote; Ambika Vishal Pawar

Addresses: Department of CSE & IT, Symbiosis International (Deemed University), Symbiosis Institute of Technology, Pune, India ' Department of CSE & IT, Symbiosis International (Deemed University), Symbiosis Institute of Technology, Pune, India

Abstract: Cloud is an umbrella wherein the internet based development and services are scrutinised and then explored. Cloud can be entitled as an enigma, wherein the novel opportunities are pioneered to manifest a large scale and flexible computing framework. The actors of a cyber supply chain can be commenced through the important functionalities such as, the utility model of consumption with elasticity, the abstraction of the framework and so on. Hybrid clouds vary greatly in sophistication facilitating portability of workloads across the entire inter-cloud, without compromising user's availability, security, or performance requirements. This paper comprehensively helps to enhance a privacy design model with cloud computing adaptation hitting the fast lane. In the first phase, the system assimilates and devises the formation of hybrid cloud architecture. In the second phase, the system implements various security tactics that are advanced encryption standard (AES) Technique, byte replacement shuffling (BRS) algorithm in consonance with sensitivity level, assigned to the file to preserve privacy. The third phase delineates the optimisation of response time (to upload and download a file) and workflow using Map-Reduce for data deduplication for a cavernous privacy and security solution.

Keywords: hybrid cloud architecture; file upload; file download; byte replacement shuffling; BRS; Map-Reduce; data deduplication; security; privacy.

DOI: 10.1504/IJAIP.2019.103414

International Journal of Advanced Intelligence Paradigms, 2019 Vol.14 No.3/4, pp.281 - 296

Received: 02 May 2016
Accepted: 07 Jan 2017

Published online: 06 Nov 2019 *

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