Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Big Data Security and Privacy: A Review on Issues, Challenges and Privacy Preserving Methods

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Anupama Jha, Meenu Dave, Supriya Madan
10.5120/ijca2017915713

Anupama Jha, Meenu Dave and Supriya Madan. Big Data Security and Privacy: A Review on Issues, Challenges and Privacy Preserving Methods. International Journal of Computer Applications 177(4):23-28, November 2017. BibTeX

@article{10.5120/ijca2017915713,
	author = {Anupama Jha and Meenu Dave and Supriya Madan},
	title = {Big Data Security and Privacy: A Review on Issues, Challenges and Privacy Preserving Methods},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2017},
	volume = {177},
	number = {4},
	month = {Nov},
	year = {2017},
	issn = {0975-8887},
	pages = {23-28},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume177/number4/28615-2017915713},
	doi = {10.5120/ijca2017915713},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In recent years the rapid growth of Internet, IOT and Cloud Computing has led to voluminous data in almost every organization, academics and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from such area around the world. Maintaining the privacy and security of Big Data is a very critical issue. The5V characteristics of big data (Volume, Variety, Velocity, Value and Veracity) alleviate the standard of security required for it. In this research paper, we have emphasized several Big Data security and privacy issues and challenges released by CSA (Cloud Security Alliance) that need to be addressed to make data processing and computing infrastructure more secure as well as possible solutions to address such challenges. This paper also gives insights on overview of big data privacy preserving K-Anonymity technique which aims to protect against leakage of individual’s identity and sensitive information before releasing the dataset during analysis. Finally, this paper overviews big data security solution application and their features provided by the top companies

References

  1. Jha A, Dave M. and Madan, S. 2016. A Review on the Study and Analysis of Big Data using Data Mining Techniques, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol6, Issue 3.
  2. Jha A, Dave M. and Madan, S. 2016. Quantitative Analysis and Interpretation of Big Data Variables in Crime Using R, International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), Vol5, Issue7.
  3. Q, etal, Jing. 2014. Security of the internet of things: perspectives and challenges. 20(8):2481–50.
  4. https://cloudsecurityalliance.org/media/news/csa-big-data-releases-top-10-security-privacy challenges/.
  5. A Cloud Security Alliance Collaborative research, Expanded Top Ten Big Data Security and Privacy Challenges. 2013.
  6. Okman, L., Gal-Oz N., Gonen Y, Gudes E. and Abramov J. 2011. Security Issues in NoSQL Databases in TrustCom IEEE Conference on International Conference on Trust, Security and Privacy in Computing and Communications, pp 541-547.
  7. Apparao, Yannam and Laxminarayanamma, Kadiyala. 2015. Security Issue on Secure Data Storage and Transaction Logs In Big Data” in International Journal of Innovative Research in Computer Science & Technology (IJIRCST).
  8. Singh, Reena and Kunver Arif Ali. 2016. Challenges and Security Issues in Big Data Analysis, IJIRSET, Vol 5. Issue 1.
  9. Sedayao, J. Enhancing cloud security using data anonymization, White Paper, Intel Corporation.
  10. Kenig Batya and Tassa Tamir. 2011. A practical approximation algorithm for optimal k-anonymity, Data Mining Knowledge Discovery, Springer.
  11. Sweeney, L. 2002. K-Anonymity: A Model for Protecting Privacy, International Journal on Uncertainty Fuzziness Knowledge based Systems.
  12. Samarati. P. 2001. Protecting respondents’ identities in microdata release. IEEE Trans. on Knowledge and Data Eng., 13:1010–1027.
  13. https://www-01.ibm.com/software/se/security/bigdata/
  14. http://www.experienceinfosys.com/iip-overview
  15. https://msdn.microsoft.com/en-us/library/dn749804.aspx
  16. www.hpe.com/hpe/jrit?
  17. https://docs.oracle.com/cd/E76178_01/
  18. https://www.thalesesecurity.com/products/data-encryption/vormetric-data-security-platform

Keywords

Big data 5V Characteristics, Security, Privacy, CSA, K-Anonymity