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Big Data Security – Challenges and Recommendations

Renu Bhandari1 , Vaibhav Hans2 , Neelu Jyothi Ahuja3

Section:Review Paper, Product Type: Journal Paper
Volume-4 , Issue-1 , Page no. 93-98, Jan-2016

Online published on Jan 31, 2016

Copyright © Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja . 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.

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IEEE Style Citation: Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja, “Big Data Security – Challenges and Recommendations,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.93-98, 2016.

MLA Style Citation: Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja "Big Data Security – Challenges and Recommendations." International Journal of Computer Sciences and Engineering 4.1 (2016): 93-98.

APA Style Citation: Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja, (2016). Big Data Security – Challenges and Recommendations. International Journal of Computer Sciences and Engineering, 4(1), 93-98.

BibTex Style Citation:
@article{Bhandari_2016,
author = {Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja},
title = {Big Data Security – Challenges and Recommendations},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2016},
volume = {4},
Issue = {1},
month = {1},
year = {2016},
issn = {2347-2693},
pages = {93-98},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=787},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=787
TI - Big Data Security – Challenges and Recommendations
T2 - International Journal of Computer Sciences and Engineering
AU - Renu Bhandari, Vaibhav Hans , Neelu Jyothi Ahuja
PY - 2016
DA - 2016/01/31
PB - IJCSE, Indore, INDIA
SP - 93-98
IS - 1
VL - 4
SN - 2347-2693
ER -

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Abstract

This paper focuses on key insights of big data architecture which somehow lead to top 5 big data security risks and the use of top 5 best practices that should be considered while designing big data solution which can thereby surmount with these risks. Big data architecture, being distributive in nature can undergo partition, replication and distribution among thousands of data and processing nodes for distributed computation thus supporting multiple features associated with big data analytics like real time, streaming and continuous data computation along with massive parallel and powerful programming framework. These series of characteristics are put into effect via a key setup that somehow leads to certain crucial security implications. The challenges induced by this can be handled via big data technologies and solutions that exist inside big data architecture compound characterized for specific big data problems. Big data solutions should provide effective ways to be more proactive against fraud, management and consolidation of data, proper security against data intrusion, malicious attacks and many other fraudulent activities. In particular, this paper discusses the issues and key features that should be taken into consideration while undergoing development of secured big data solutions and technologies that will handle the risks and privacy concerns (e.g. Data security, insecure computation and data storage, invasive marketing etc.) associated with big data analysis in an effective way to increase the performance impact, considering that these risks are somehow a result of characteristics of big data architecture.

Key-Words / Index Term

Big Data; Hadoop; MapReduce; Secure Computation

References

[1] Big data. In Wikipedia, The Free Encyclopedia. Retrieved 08:36, November10, 2015
[2] Apache Hadoop. In Wikipedia, The Free Encyclopedia. Retrieved 10:28,November 20,2015
[3] MapReduce. In Wikipedia, The Free Encyclopedia. Retrieved 08:43, January 15, 2016
[4] IBM Security Intelligence with Big Data, In IBM. Retrieved 09:38, November 22, 2015
[5] Big Data Research, Security in big data research papers, Retrieved 08:10, December 10,2015
[6] Anuja Pandit, Amruta Deshpande and Prajakta Karmarkar, Log Mining Based on Hadoop’s Map and Reduce Technique, Int. Journal of Computer Sciences and Engineering, Volume -05, Issue -04, Page No (1-4), April 2013