Security Challenges Associated with High Dimensional Data

Authors

  • Tata Gayathri  Assistant Professor, Department of CSE, Shri Vishnu engineering college for women, Bhimavaram, Andhra Pradesh, India
  • N Durga  Assistant Professor, Department of CSE, Shri Vishnu engineering college for women, Bhimavaram, Andhra Pradesh, India

Keywords:

Big Data, security, privacy, security Practices

Abstract

Big data implies performing computation and database operations for massive amounts of data, remotely from the data owner’s enterprise. Since a key value proposition of big data is access to data from multiple and diverse domains, security and privacy will play a very important role in big data research and technology. The variety, velocity and volume of big data amplifies security management challenges that are addressed in traditional security management. Big data repositories will likely include information deposited by various sources across the enterprise. This variety of data makes secure access management a challenge. Each data source will likely have its own access restrictions and security policies, making it difficult to balance appropriate security for all data sources with the need to aggregate and extract meaning from the data.

References

  1. Mayer-Schonberger, V.; Cukier, K. BigData:RevolutionthatWillTransformHowWeLive,Work,andThink; Houghton Mifflin Harcourt: Boston, MA, USA, 2013.
  2. Sagiroglu, S.; Sinanc, D. Big data: A review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 20-24 May 2013; pp. 42-47.
  3. Hashem, I.A.T.; Yaqoob, I.; Anuar, N.B.; Mokhtar, S.; Gani, A.; Ullah Khan, S. The rise of "big data" on cloud computing: Review and open research issues. Inf. Syst. 2015, 47, 98-115. [CrossRef]
  4. Sharma, S. Rise of Big Data and related issues. In Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India, 17-20 December 2015; pp. 1-6.
  5. Eynon, R. The rise of Big Data: What does it mean for education, technology, and media research? Learn. Media Technol. 2013, 38, 237-240. [CrossRef]
  6. Wang, H.; Jiang, X.; Kambourakis, G. Special issue on Security, Privacy and Trust in network-based Big Data. Inf. Sci. Int. J. 2015, 318, 48-50. [CrossRef]
  7. Thuraisingham, B. Big data security and privacy. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, San Antonio, TX, USA, 2-4 March 2015; pp. 279-280.
  8. Rijmenam, V. ThinkBigger: DevelopingaSuccessful Big DataStrategyfor YourBusiness; Amacom: New York, NY, USA, 2014.
  9. Big Data Working Group; Cloud Security Alliance (CSA). Expanded Top Ten Big Data Security and Privacy. April 2013. Available onlinehttps://downloads.cloudsecurityalliance.org/initiatives/bdwg/Expanded_ Top_Ten_Big_Data_Security_and_Privacy_Challenges.pdf (accessed on 9 December 2015).
  10. Meng, X.; Ci, X. Big data management: Concepts, techniques and challenges. Comput. Res. Dev. 2013, 50, 146-169.
  11. Xu, L.; Jiang, C.; Chen, Y.; Ren, Y.; Liu, K.J.R. Privacy or Utility in Data Collection? A Contract Theoretic Approach. IEEE J. Sel. Top. Signal Proc. 2015, 9, 1256-1269.
  12. Cheng, H.; Rong, C.; Hwang, K.; Wang, W.; Li, Y. Secure big data storage and sharing scheme for cloud tenants. China Commun. 2015, 12, 106-115. [CrossRef]
  13. Weber, A.S. Suggested legal framework for student data privacy in the age of big data and smart devices. In Smart Digital Futures; IOS Press: Washington, DC, USA, 2014; Volume 262.
  14. Thilakanathan, D.; Calvo, R.; Chen, S.; Nepal, S. Secure and controlled sharing of data in distributed computing. In Proceedings of the 16th IEEE International Conference on Computational Science and Engineering (CSE 2013), Sydney, Australia, 3-5 December 2013; pp. 825-832.
  15. Chen,J.;Liang,Q.;Wang,J.Securetransmissionforbigdatabasedonnestedsamplingandcoprimesampling with spectrum efficiency. Secur. Commun. Netw. 2015, 8, 2447-2456. [CrossRef]
  16. Liu, C.; Yang, C.; Zhang, X.; Chen, J. External integrity verification for outsourced big data in cloud and IoT. Future Gener. Comput. Syst. 2015, 49, 58-67. [CrossRef]
  17. Wang, Y.; Wei, J.; Srivatsa, M.; Duan, Y.; Du, W. IntegrityMR: Integrity assurance framework for big data analytics and management applications. In Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, 6-9 October 2013; pp. 33-40.
  18. Liao, C.; Squicciarini, A. Towards provenance-based anomaly detection in MapReduce. In Proceedings of the 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Shenzhen, China, 4-7 May 2015; pp. 647-656.
  19. Tan, Z.; Nagar, U.T.; He , X.; Nanda, P.; Liu, R.P.; Wang, S.; Hu, J. Enhancing big data security with collaborative intrusion detection. IEEE Cloud Comput. 2014, 1, 27-33. [CrossRef]
  20. Chang, V. Towards a Big Data system disaster recovery in a Private Cloud. Ad Hoc Netw. 2015, 35, 65-82. [CrossRef]
  21. J.Z. Huang, M.K. Ng, H. Rong, and Z. Li, "Automated Variable Weighting in k-Means Type Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 1-12, May 2005.
  22. L. Jing, M.K. Ng, J. Xu, and J.Z. Huang, "Subspace Clustering of Text Documents with Feature Weighting k-Means Algorithm," Proc. Ninth Pacific-Asia Conf. Knowledge Discovery and Data Mining, pp. 802-812, 2005.
  23. L. Parsons, E. Haque, and H. Liu, "Subspace Clustering for High Dimensional Data: A Review," SIGKDD Explorations, vol. 6, no. 1, pp. 90-105, 2004.
  24. C.H. Cheng, A.W. Fu, and Y. Zhang, "Entropy-Based Subspace Clustering for Mining Numerical Data," Proc. Fifth ACM SIGKDD Int’l Conf. Knowledge and Data Mining, pp. 84-93, 1999.
  25. S. Goil, H. Nagesh, and A. Choudhary, "Mafia: Efficient and Scalable Subspace Clustering for Very Large Data Sets," Technical Report CPDC-TR-9906-010, Northwest Univ., 1999.
  26. R.T. Ng and J. Han, "Efficient and Effective Clustering Methods for Spatial Data Mining," Proc. 20th Int’l Conf. Very Large Data Bases, pp. 144-155, Sept. 1994.
  27. G. De Soete, "Optimal Variable Weighting for Ultrametric and Additive Tree Clustering," Quality and Quantity, vol. 20, pp. 169180, 1986.
  28. Big Data: Issues and Challenges Moving Forward. 2013 46th Hawaii International Conference on System Sciences Stephen

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Published

2017-08-31

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Section

Research Articles

How to Cite

[1]
Tata Gayathri, N Durga, " Security Challenges Associated with High Dimensional Data, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.534-540, July-August-2017.