Big data life cycle: security issues, challenges, threat and security model
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2017-12-31 https://doi.org/10.14419/ijet.v7i1.3.9666 -
Big Data, Challenges, Issues, Privacy, Security Life Cycle. -
Abstract
Today the technologies of big data are completely bringing a vast change in the entire conventional technology discipline and it’s successfully applying the required latest security design methods to state the upcoming security provocations. Big Data Architecture is a “Data†centric architecture in which security can be included in all the levels. Data is collected from different sources and Data generation is done, the next step it undergoes is Data Processing, the next step is Data storage and the last step is Data analysis. At all the levels Data plays a vital role. It aims to give basic investigation regarding most of the security risks and Big Data provocation and bought out new provocations, complication to the conventional protective domains and also for conventional trends. This deals with the definition of big data and the characteristics that effect most of the data preservation, such as 3V’s, dynamicity. It analyses the original changes and new challenges to Data security. It also provides pitch for real time practice of security infrastructure peripherals which allows extend trusted non-local virtualized processing environment. This research focus on all levels of Big Data where and when the security services and techniques can be included to acquire accurate results.
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References
[1] Big data analytics: organizational factor matters impact technology acceptance, Journal of Big Data, Springer Open Access, June 2017.
[2] Big data privacy: A technological perspective and review, Journal of Big Data, Springer Open Access, November, 2016.
[3] An efficient strategy for the collection and storage of large volumes of data for computation, Journal of Big Data, Springer Open Access, October 2016.
[4] Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities, Big Data Research, Elsevier, December 2016.
[5] Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems, Big Data Research, Elsevier, December 2015.
[6] Big Scholarly Data: A Survey, IEEE Transaction on Big Data, Vol 3, No.1, March 2017.
[7] Big Data for Cyber Security: Vulnerability Disclosure trends and Dependencies, IEEE Transaction on Big Data, Vol 3, No.1, October 2016.
[8] Methodologies for cross-domain data fusion, IEEE Transaction on Big Data, Vol 1, No.1, January 2015.
[9] Efficient big data processing in Hadoop MapReduce, Journal proceedings of the VLDB Endowment, ACM, Vol 5, Issue 12, August 2015.
[10] Z. Wu et al. "Towards building a scholarly big data platform: Challenges lessons and opportunities" pp. 117-126 2014
[11] Y.-R. Lin H. Tong J. Tang K. S. Candan "Guest editorial: Big scholar data discovery and collaboration" vol. 2 no. 1 pp. 1-2 Jan.-Mar. 2016.
[12] S. Kaisler F. Armour J. A. Espinosa W. Money "Big data: Issues and challenges moving forward". IEEE 46th Hawaii Int. Conf. Syst. Sci. </em> pp. 995-1004 2013.
[13] S. Sagiroglu D. Sinanc "Big data: A review". IEEE Int. Conf. Collaboration Technol. System pp. 42-47 2013.
[14] J. Dean and S. Ghemawat. MapReduce: A Flexible Data Processing Tool. CACM, 53(1):72–77, 2010.https://doi.org/10.1145/1629175.1629198.
[15] S. Blanas et al. A Comparison of Join Algorithms for Log Processing in MapReduce. In SIGMOD, pages 975–986, 2010.https://doi.org/10.1145/1807167.1807273.
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How to Cite
SankaramAlladi, B., & Srinivas Prasad, D. (2017). Big data life cycle: security issues, challenges, threat and security model. International Journal of Engineering & Technology, 7(1.3), 100-103. https://doi.org/10.14419/ijet.v7i1.3.9666Received date: 2018-02-22
Accepted date: 2018-02-22
Published date: 2017-12-31