International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.11(9) (2020)

  • TECHNOLOGICAL TOOLS FOR DATA SECURITY IN THE TREATMENT OF DATA RELIABILITY IN BIG DATA ENVIRONMENTS

    Luay Abdulwahid Shihab (Department of Basic Science, College of Nursing, University of Basrah, Basrah, IRAQ).

    Disciplinary: Computer Sciences and Information Technology.

    Full Text

    DOI: 10.14456/ITJEMAST.2020.175

    Keywords: Information security; Data authenticity; Technological security tools; Reliability of data; Information security mechanism; Big Data technologies; Data legality; Network security measures; Veracity in Big Data; Data privacy.

    Abstract
    The set of new technological solutions that allow organizations to better manage their information, commonly known as "Big Data", have a growing role in all types of public and private organizations. As a Big Data problem, how data grows in volume, speed and variety can be contemplated. This is due to the great advance and use of information technologies, and the daily use that people make of them. Within the state of the art are found from various definitions of the term Big Data to existing technologies to start a project in an institution of any productive, commercial, or educational branch. This article gives an overview of the data security technology processes, defining those that lead to rising data veracity in Big Data environments. As a result of this analysis, a series of criteria was established relevant to the authenticity of the data and the use of network security measures were suggested for each of these criteria. The article also seeks to lead to further work on information security within Information Science, as it would provide a perspective on the methods available for approaching information security, leading to increasing the reliability of knowledge obtained from contexts containing significant volumes of knowledge. This work proposes adding two criteria for veracity, highlighted as a contribution of this work, in addition to the previous criteria. These are legality and privacy.

    Paper ID: 11A9M

    Cite this article:

    Shihab, L. A. (2020). TECHNOLOGICAL TOOLS FOR DATA SECURITY IN THE TREATMENT OF DATA RELIABILITY IN BIG DATA ENVIRONMENTS. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(9), 11A9M, 1-13. http://doi.org/10.14456/ITJEMAST.2020.175



References

  1. Aalst, W. M. P. (2014). Data Scientist: The Engineer of the Future. In Enterprise Interoperability VI(7), K.Mertins, F. Benaben, R. Poler, and J. P. Bourrieres, Eds. Springer, 13-26.
  2. Ahmed, E. et al. (2018). Recent Advances and Challenges in Mobile Big Data. IEEE Communications. 56. DOI: 10.1109/MCOM.2018.1700294.
  3. Arnaboldi, M., Busco, C., Cuganesan, S. (2017). Accounting, accountability, social media and big data: Revolution or hype. Account. Audit. Account. J. 30, 762-776.
  4. Bahga, A., & Madisetti, V. K. (2011). Analyzing massive machine maintenance data in a computing cloud. IEEE Transactions on Parallel and Distributed Systems, 23(10), 1831-1843.
  5. Bhadani, A., Jothimani, D. (2016). Big data: Challenges, opportunities and realities, In Singh, M.K., & Kumar, D.G. (Eds.), Effective Big Data Management and Opportunities for Implementation (1-24).
  6. Bhogal, N. & Jain, S. (2017). A review on big data security and handling. 6.
  7. DeCandia, G., et al. (2007). Dynamo: amazon’s highly available key-value store. SOSP, 7, 205-220.
  8. Deka, G. C. (2013). A survey of cloud database systems. IT Professional, 16(2), 50-57.
  9. EU Commission (2013). “Big Data. Analytics & Decision Making”. Business Innovation Observatory.
  10. Feng, D. G., Zhang, M., & Li, H. (2014). Trusted Computing and Information Assurance Laboratory, Institute of Software, Chinese Academy of Sciences. Big Data Security and Privacy Protection. 37(1), 246-258.
  11. Gill, J., & Singh, S. (2015). Enormous Possibilities in Big Data: Trends and Applications. Asian Journal of Computer Science and Technology, 4(2), 23-26.
  12. Harfoushi, O., Obiedat, R. (2018). Security in Cloud Computing Using Hash Algorithm: A Neural Cloud Data Security Model. Modern Applied Science. DOI: 10.5539/mas.v12n6p143.
  13. Hashizume K. Rosado D. G. Fernandez-Medina E. Fernandez E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(1), 1-13. 10.1186/1869-0238-4-5
  14. Hu, V. et al. (2014). An Access Control Scheme for Big Data Processing. DOI: 10.4108/icst.collaboratecom.2014.257649.
  15. Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security issues associated with big data in cloud computing. International Journal of Network Security & Its Applications, 6(3), 45-56.
  16. Jain, P. et al. (2016). Big data privacy: a technological perspective and review. Journal of Big Data. 3. DOI: 10.1186/s40537-016-0059-y
  17. Ji, Y., Y. Tian, F. Shen, and J. Tran. (2016). Experimental Evaluations of MapReduce in Biomedical Text Mining. Information Technology: New Generations. Springer. 665-675.
  18. K.C. Li, H. Jiang, L. T. Yang, and A. Cuzzocrea. (2015). Big Data: Algorithms, Analytics, and Applications. Chapman & CRC Press.
  19. Kashyap, Ramgopal & Piersson, A. (2018). Impact of Big Data on Security (Chapter 15). 10.4018/978-1- 5225-4100-4.ch015
  20. Klein, M., et al. (2017). Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark. Bioinformatics, 33(2), 303-305.
  21. L. Douglas. (2001). 3D data management: Controlling data volume, velocity and variety. Gartner.
  22. LiYi, Keke Gai,Longfei Qiu, Meikang Qiu, ZhaoHuid, Intelligent cryptography approach for secure distributed big data storage in cloud computing, Information Sciences, Volume 387, May 2017, Pages 103-115.
  23. Luaay Abdulwahed Shihab. (2012). Wireless LAN Security and Management, International Journal of Engineering and Advanced Technology (IJEAT), 2(1).
  24. Manogaran, G., Thota, C., & Kumar, M. V. (2016). MetaCloudDataStorage architecture for big data security in cloud computing. Procedia Computer Science, 87, 128-133.
  25. Marchal, S., Jiang, X., State, R., & Engel, T. (2014). A Big Data Architecture for Large Scale Security Monitoring. In IEEE Big Data Congress, 56-63. DOI: IEEE. 10.1109/BigData.Congress.2014.18
  26. Matturdi, Bardi & Zhou, Xianwei & Li, Shuai & Lin, Fuhong. (2014). Big Data security and privacy: A review. China Communications. 11. DOI: 135-145. 10.1109/CC.2014.7085614.
  27. Mohanty, H., Bhuyan, P., & Chenthati, D. (Eds.). (2015). Big data: A primer. 11, Springer.
  28. Oussous, A., et al. (2017). Big Data technologies: A survey. Journal of King Saud University - Computer and Information Sciences. DOI: 10.1016/j.jksuci.2017.06.001
  29. Rubin, V., & Lukoianova, T. (2014). Veracity roadmap: Is big data objective, truthful and credible? Advances in Classification Research Online, 24(1), 4-15.
  30. Singh, S. and N. Ahuja. (2015). Article recommendation system based on keyword using map-reduce. In 2015 Third International Conference on Image Information Processing (ICIIP), pp. 548-550.
  31. Sabahi, F. (2011). Virtualization-level security in cloud computing. In Communication Software and Networks, IEEE 3rd International Conference. DOI: IEEE. 10.1109/ICCSN.2011.6014716
  32. Singh, S., & Ahuja, N. (2015). Article recommendation system based on keyword using map-reduce. In IEEE Third International Conference on Image Information Processing (ICIIP) (548-550).
  33. Tanwar, S., Prema, V. (2014). Role of Public Key Infrastructure in Big Data Security. CSI Communications, 45-48.
  34. Tian, Y. (2017). Towards the Development of Best Data Security for Big Data. Communications and Network. 09. 291-301. DOI: 10.4236/cn.2017.94020.
  35. Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D. (2016). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ.165, 234-246.
  36. Ye, F., Y. Qian, R.Q. Hu. (2015). An Identity-Based Security Scheme for a Big Data Driven Cloud Computing Framework in Smart Grid. 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, 1-6.
  37. Zhang, Yingfeng & Ren, Shan & Liu, Yang & Si, Shubin. (2016). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production. DOI: 10.1016/j.jclepro.2016.07.123
  38. Zikopoulos, P., Eaton, C., et al. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.


Other issues:
Vol.11(11)(2020)
Vol.11(10)(2020)
Vol.11(8)(2020)
Vol.11(7)(2020)
Vol.11(6)(2020)
Vol.11(5)(2020)
Vol.11(4)(2020)
Vol.11(3)(2020)
Vol.11(2)(2020)
Vol.11(1)(2020)
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