DDDA: Development of a Distributed De-Duplication Approach using Big Data Analysis in Hybrid Cloud Environment

Aarfa Khan¹ , Snehlata Kothari²

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.4 , pp.18-21, Aug-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i4.1821

Online published on Aug 31, 2019

Copyright © Aarfa Khan, Snehlata Kothari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

451 Views    336 Downloads    104 Downloads

Abstract :
A renewed interest in cloud computing adoption has occurred in academic and industry settings because emerging technologies have strong links to cloud computing and Big Data technology. Big Data technology is driving cloud-computing adoption in large business organizations. For cloud computing adoption to increase, cloud computing must transition from low-level technology to high-level business solutions. Security, privacy and elimination of repetitive copies of data is of primary concern for many applications of Big Data (BD). Data of the consumers must be protected else private information can be leaked. Cloud should let the owners or a trusted third party to check for the integrity of their data storage without demanding a local copy of the data. For this reason, this paper covered: Issues in big data management (BDM), secure data processing (DP) and access control (AC’s) of data in cloud by data owner, data integrity verification in cloud. On performance basis, proposed approach is tested and simulated on different raw data and their processing compared with few of existing algorithm based on security, accessibility and integrity parameters. Results obtained are satisfactory to achieve all in single approach.

Key-Words / Index Term :
DDDA, Big Data, Security, Cloud Computing, BDM etc

References :
[1] Towards the Design of a System and a Workflow Model for Medical Big Data Processing in the Hybrid Cloud :2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
[2] Geospatial Big Data Processing in Hybrid Cloud Environments: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
[3] Data security in cloud: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
[4] SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments:2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing
[5] Economical and efficient big data sharing with i-Cloud:2014 International Conference on Big Data and Smart Computing (BIGCOMP)
[6] Rao, R. V., & Selvamani, K. (2015). Data security challenges and its solutions in cloud computing. Procedia Computer Science, 48, 204–209.
[7] Saad, S. A., Adam, A., & Abdelateef, A. H. (2016). Binary logistic regression to estimate household income efficiency (South Darfur rural areas-Sudan). International Journal of Advanced Statistics and Probability, 4(1), 31–35. doi: 10.14419/ijasp.v4i1.5657
[8] Schroedl, J. (2016). Hyperconverged infrastructure meets Big-Data-as-a-service.Bluedata. Retrieved from https://www.bluedata.com/blog/2016/05/hyperconverged-infrastructure-meets-big-data/
[9] Shahrivari, S. (2014). Beyond batch processing: towards real-time and streaming Big Data. Computers, 3(4), 117-129.Siegel, E. (2013a). Predictive analytics. Hoboken, NJ: John Wiley & Sons.
[10] Siegel, E. (2013b, July/August). Predictive analytics: Harnessing the power of big data. Retrieved from http://analytics-magazine.org/predictive-analytics-2/Singh, J. (2017). Study on challenges, opportunities and predictions in cloud computing. International Journal of Modern Education and Computer Science, 9(3), 17.