Security issues of big data analytics

Neha Khalid; Mishaal Sikander; Soha Bint Zarar Khan; Muhammad Rizwan; Fahad Ahmad

Author(s)

Neha Khalid Department of Computer Sciences, Kinnaird College for Women,Lahore,Pakistan

Mishaal Sikander Department of Computer Sciences, Kinnaird College for Women,Lahore,Pakistan

Soha Bint Zarar Khan Department of Computer Sciences, Kinnaird College for Women,Lahore,Pakistan

Muhammad Rizwan Department of Computer Sciences, Kinnaird College for Women,Lahore,Pakistan

Fahad Ahmad Department of Computer Sciences, Kinnaird College for Women,Lahore,Pakistan

Abstract

The world in which we are living today is full of technology and digitalization, due to this our daily activities have resulted in large amount of data, this huge amount of unprocessed information is mainly from social networking sites and collectively called Big Data. Big Data is used by many organizations through which marketing decisions are made and behavior of data is detected. This “Big Data” is controlled and maintained by Big Data Analytics. Organizations face challenges to maintain huge amount of data because of current inefficient methodologies. And this fact is emerging as a challenge and also risk to security. Considering these issues, strong techniques need to be proposed immediately. This paper highlights major security issues of big data analytics. Later a solution to overcome these big data analytics security issues is proposed.

Publication Details

Page(s) 148-153

DOI DOI not available

Published Journal: Journal of Natural & Applied Sciences Pakistan, Volume: 1, Issue: 2, Year: 2019

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Keywords

Big Data, big data analytics, Big Data Analytics, security issue, COUBF, analytics variants, bloom filters

References

Gani A.,A. S. A.,Systems Information .2015 .A survey on indexing techniques for big data: taxonomy and performance evaluation. , 46 : 241-284.

Alsuhibany S. A. (n.d.). . .A space-and-time efficient technique for big data security analytics. , : 1-6.

Cárdenas Alvaro A.,P. K. Alvaro A.,IEEE Alvaro A. .2016 .Privacy. , : 74-76.

Dopazo J. .2014 .Genomics and transcriptomics in drug discovery,. , 19 : 126-132.

Dubey H.,J. Y. H. .2015 .Fog Data: Enhancing Telehealth Big Data Through Fog Computing. ACM ASE BigData & SocialInformatics, : 1-6.

Nivash J.P.,E. D. J.P. .2014 .Analysis on enhancing storm to efficiently process big data in real time. Proceedings of the 2014 International Conference on Computing, Communication, : 1-5.

Gärtner M.,A. R. M.,Systems Information .2013 .Bridging structured and unstructured data via hybrid semantic search and interactive ontologyenhanced query formulation. , 41 : 761-792.

Weidner M.,J. D. M. .2013 .Fast OLAP Query Execution in Main Memory on Large Data in a Cluster. IEEE International Conference on Big Data, : 518-524.

Buyya R.,K. R. R. .2015 .Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions. , 1510 : 06486.

Manegold S.,P. B. S. .2002 .Optimizing mainmemory join on modern hardware. TKDE, 14 : 709-730.

X. Wu X. Z.-Q. .2014 .Data mining with big data," In IEEE Transactions on Knowledge and Data Engineering. , 26 : 97-107.

Zhang X.,T. K. X. .2004 .Strategies for using additional resources in parallel hash-based join algorithms. , : 4-13.