Title: Data anonymisation of vertically partitioned data using Map Reduce techniques on cloud

Authors: Thangaramya Kalidoss; Ganapathy Sannasi; Sairamesh Lakshmanan; Kulothungan Kanagasabai; Arputharaj Kannan

Addresses: Department of Information Science and Technology, Anna University, CEG Campus, Chennai, 600 025, India; School of Computing Science and Engineering, VIT University, Chennai Campus, Chennai, 600 127, India ' Department of Information Science and Technology, Anna University, CEG Campus, Chennai, 600 025, India; School of Computing Science and Engineering, VIT University, Chennai Campus, Chennai, 600 127, India ' Department of Information Science and Technology, Anna University, CEG Campus, Chennai, 600 025, India; School of Computing Science and Engineering, VIT University, Chennai Campus, Chennai, 600 127, India ' Department of Information Science and Technology, Anna University, CEG Campus, Chennai, 600 025, India; School of Computing Science and Engineering, VIT University, Chennai Campus, Chennai, 600 127, India ' Department of Information Science and Technology, Anna University, CEG Campus, Chennai, 600 025, India; School of Computing Science and Engineering, VIT University, Chennai Campus, Chennai, 600 127, India

Abstract: Data anonymisation in cloud is used for enhancing the security which is effectively implemented using vertical fragmentation along with encryption and compression techniques. In the existing cloud databases, distributed storage and processing of data are performed by applying fragmentation and search techniques. In order to improve the performance of distributed access plans, Map Reduce techniques can be used which provide fast search and retrieval. Hence, a new distributed security processing mechanism is proposed in this paper to enhance the security of cloud networks which consists of two phases namely encryption and verification phases. For this purpose, a new triple advanced encryption standard algorithm is proposed in this paper that performs secured key distribution based on verification using secured hash algorithm. Moreover, this work uses Map Reduce techniques for fast processing. Experimental results of this work show that our approach provides higher security and effective storage over the existing approaches.

Keywords: data anonymisation; top-down specialisation; Map Reduce; cloud; privacy preservation.

DOI: 10.1504/IJCNDS.2018.092147

International Journal of Communication Networks and Distributed Systems, 2018 Vol.20 No.4, pp.519 - 531

Accepted: 24 Jun 2017
Published online: 05 Jun 2018 *

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