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Implementing Integrity Assurance System for Big Data

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Abstract

The computation of big data and related services has been the topic of research and popular applications due to the rapid progress of big data technology and statistical data analysis solutions. There are several issues with data quality that contribute to error decisions in organizations and institutions. Current research just covers how to adequately validate data to assure its validity. Data integrity is synonymous with data validity. It is a difficult undertaking that is often performed by national statistics organizations and institutes. There is a significant need to provide a general system for validating the big data integrity. This approach has been dedicated to presenting a model for data integrity, particularly big data, and how to solve the validation process. The data also comprises the validity of the data fields, as well as the validity of measuring the data and determining compliance with the data cycle chain. For the integrity of large data, the processing speed and accuracy of the verification process are taken into account. The research was based on the Python programming language and real test data, and it was based on the use of the most recent technologies and programming languages.

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References

  1. Kim, G.-H., Trimi, S., & Chung, J.-H. (2014). Big-data applications in the Government sector. Communications of the ACM, 57(3), 78–85.

    Article  Google Scholar 

  2. Nath, A. (2015). Big data security issues and challenges. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(2), 15–20.

    Google Scholar 

  3. 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 (IJNSA), 6(3), 45–56.

    Article  Google Scholar 

  4. Lebdaoui, S. El Hajji and Orhanou, G., "Managing big data integrity," in 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco, 2016.

  5. Liu, C., Ranjan, R., Zhang, X., Yang, C, Georgakopoulos, D, and Chen, J. (2013) "Public Auditing for Big Data Storage in Cloud Computing - A Survey," 2013 IEEE 16th International Conference on Computational Science and Engineering.

  6. Demchenko, Y., Membrey, C. de Laat, P., and Grosso, P., (2013) “Addressing Big Data Issues in Scientific Data Infrastructure,” in 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA.

  7. Lagoze, C. (2014). “Big Data, data integrity, and the fracturing of the control zone.” Big Data & Society, 1–11.

  8. Sing, A. P., & Pasupuleti, S. K. (2016). Optimized public auditing and data dynamics for data storage security in cloud computing. Procedia Computer Science, 93, 751–759.

    Article  Google Scholar 

  9. Liu, B., Yu, X. L., Chen, S., Xu, X., and Zhu, L., (2017) "Blockchain-Based Data Integrity Service Framework for IoT Data," 2017 IEEE International Conference on Web Services (ICWS), pp. 468–475.

  10. Li, Y., Yu, Y., Min, G., Susilo, W., Ni, J., & Choo, K.-K.R. (2019). Fuzzy identity-based data integrity auditing for reliable cloud storage systems. IEEE Transactions on Dependable and Secure Computing, 16(1), 72–83.

    Article  Google Scholar 

  11. Iarocci, N., "Cerberus," 2016. [Online]. Available: python-cerberus.org.

  12. Zhou, L., Fu, A., Yu, S., Su, M., & Kuang, B. (2018). Data integrity verification of the outsourced big data in the cloud environment. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2018.08.003

  13. Mantzoukas, K., Kloukinas, C., & Spanoudakis, G. (2018). Monitoring Data Integrity in Big Data Analytics Services. IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 904–907). IEEE.

  14. Mukhtaj, K., Maozhen, L., Phillip, A., Gareth, T., & Junyong, L. (2014). Big Data Analytics on PMU Measurements. 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2014. p. 715–719.

  15. Peng, S., Zhou, F., & Wang, Q. (2017). Identity-based public multi-replica provable data possession. IEEE Access, 5, 26990–27001.

    Article  Google Scholar 

  16. Sookhak, M., Yu, F. R., & Zomaya, A. Y. (2018). Auditing big data storage in cloud computing using divide and conquer tables. IEEE Trans Parallel Distr. Syst, 29(5), 999–1012.

    Article  Google Scholar 

  17. Yin, S. (2020). Research on the detection algorithm of data integrity verification results in big data storage. J Phys: Conf Ser., 1574, 012008.

    Google Scholar 

  18. Rai, S., & Sharma, A. (2020). Research perspective on security-based algorithm in big data concepts. International Journal of Engineering and Advanced Technology, 9(3), 2138–2143.

    Article  Google Scholar 

  19. Manimurugan, S. (2021). IoT-fog-cloud model for anomaly detection using improved naive bayes and principal component analysis. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02723-3

    Article  Google Scholar 

  20. Manimurugan, S., Majdi, A.-Q., Mohmmed, M., Narmatha, C., & Varatharajan, R. (2020). “Intrusion detection in network, adaptive neuro-fuzzy inference system-ANFIS, crow search optimization- CSO, NSL-KDD". Microprocessors and Microsystems, 79, 103261. https://doi.org/10.1016/j.micpro.2020.103261

    Article  Google Scholar 

  21. Narmatha, C., Eljack, S. M., Tuka, A. A. R. M., et al. (2020). A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI Images. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02470-5

  22. Manimurugan, S., Almutairi, S., Aborokbah, M., Ganesan, S., Varatharajan, R. (2020). A review on advanced computational approaches on multiple sclerosis segmentation and classification. IET signal Processing, 14(6), 333–341. https://doi.org/10.1049/iet-spr.2019.0543

  23. Manimurugan, S., Almutairi, S., Mohammed Aborokbah, M., Ganesan, N. C. S., & Patan, R. (2020). Effective attack detection in internet of things smart environment using deep belief neural network. IEEE Access., 8, 77396–77404.

    Article  Google Scholar 

  24. Almutairi, S., Manimurugan, S., & Aborokbah, M. (2019). A new secure transmission scheme between senders and receiver using HVCHC without Any Loss. EURASIP Journal on Wireless Communications and Networking, 2019, 88. https://doi.org/10.1186/s13638-019-1399-z

    Article  Google Scholar 

  25. Al-Mutairi, S., & Manimurugan, S. (2017). The clandestine image transmission scheme to prevent from the intruders. International Journal of Advanced and Applied Sciences, 4(2), 52–60.

    Article  Google Scholar 

  26. Manimurugan, S., & Mutari, S. A. (2017). A novel secret image hiding technique for secure transmission. Journal of Theoretical and Applied Information Technology, 95(1), 166–176.

    Google Scholar 

  27. Manimurugan, S., & Narmatha, C. (2015). Secure and efficient medical image transmission by new tailored visual cryptography scheme with LS compressions. International Journal of Digital Crime and Forensics (IJDCF), 7(1), 26–50.

    Article  Google Scholar 

  28. Manimurugan, S., Porkumaran, K., & Narmatha, C. (2014). The new block pixel sort algorithm for TVC encrypted medical image. Imaging Science Journal., 62(8), 403–414.

    Article  Google Scholar 

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Authors and Affiliations

  1. Master of Information Security, Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Saudi Arabia

    Fawaz Alyami

  2. Industrial Innovation & Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk City, Saudi Arabia

    Saad Almutairi

Authors
  1. Fawaz Alyami
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  2. Saad Almutairi
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Fawaz and Saad Almutairi are contributed equally.

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Correspondence to Saad Almutairi.

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The authors declare no conflict of interest, financial or otherwise.

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Alyami, F., Almutairi, S. Implementing Integrity Assurance System for Big Data. Wireless Pers Commun 122, 2585–2601 (2022). https://doi.org/10.1007/s11277-021-09013-x

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