Skip to main content

Big Data Security and Privacy Preservation Techniques for Distributed Cloud Computing Environments

Pragati Birhade

MMCOE, Pune, Maharashtra, India

Abstract

The rapid expansion of big data technologies and distributed cloud computing environments has transformed modern information systems by enabling scalable storage, processing, and analysis of massive datasets. Organizations across healthcare, finance, education, government, and e-commerce sectors increasingly rely on distributed cloud infrastructures to manage complex data-intensive applications. However, the growth of big data ecosystems has introduced significant challenges related to data security, privacy preservation, unauthorized access, cyberattacks, and regulatory compliance. Distributed cloud environments involve multiple interconnected data centers, virtualized resources, and shared infrastructures, which increase the risks associated with data breaches and information leakage. This study explores advanced security and privacy preservation techniques designed to protect sensitive information within distributed cloud computing systems. The research focuses on encryption methods, access control mechanisms, blockchain integration, anonymization techniques, intrusion detection systems, secure multi-party computation, and artificial intelligence-based threat detection approaches. The study also examines the impact of data governance policies, regulatory frameworks, and trust management strategies on cloud security. A comprehensive research methodology involving architectural analysis, simulation modeling, comparative evaluation, and performance assessment is adopted to investigate the effectiveness of various security mechanisms. The findings demonstrate that integrated multi-layered security frameworks significantly improve confidentiality, integrity, availability, and privacy preservation in distributed big data cloud environments

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 3 No. 6 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2018.0306001

Pages

4465-4474

Published

November 11, 2018
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Pragati Birhade (%2018). Big Data Security and Privacy Preservation Techniques for Distributed Cloud Computing Environments. International Journal of Emerging Trends in Engineering and Management Research , Vol. 3 No. 6 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 4465-4474. https://doi.org/10.15662/ijetemr.2018.0306001

References

1. Abawajy, J. (2014). User preference of cyber security awareness delivery methods. Behaviour & Information Technology, 33(3), 237–248.
2. Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM SIGMOD Record, 29(2), 439–450.
3. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
4. Benaloh, J. (1994). Dense probabilistic encryption. In Proceedings of the Workshop on Selected Areas of Cryptography (pp. 120–128).
5. Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud computing: Principles and paradigms. Wiley.
6. Dwork, C. (2008). Differential privacy: A survey of results. In Proceedings of the International Conference on Theory and Applications of Models of Computation (pp. 1–19).
7. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In Proceedings of the ACM Symposium on Theory of Computing (pp. 169–178).
8. Jensen, M., Schwenk, J., Gruschka, N., & Iacono, L. L. (2009). On technical security issues in cloud computing. In IEEE International Conference on Cloud Computing (pp. 109–116).
9. Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014, 1–18.
10. Li, M., Yu, S., Zheng, Y., Ren, K., & Lou, W. (2013). Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Transactions on Parallel and Distributed Systems, 24(1), 131–143.
11. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology.
12. Popa, R. A., Redfield, C. M. S., Zeldovich, N., & Balakrishnan, H. (2011). CryptDB: Protecting confidentiality with encrypted query processing. In Proceedings of the ACM Symposium on Operating Systems Principles (pp. 85–100).
13. Ren, K., Wang, C., & Wang, Q. (2012). Security challenges for the public cloud. IEEE Internet Computing, 16(1), 69–73.
14. Shamir, A. (1984). Identity-based cryptosystems and signature schemes. In Advances in Cryptology (pp. 47–53).
15. Wang, C., Wang, Q., Ren, K., Cao, N., & Lou, W. (2012). Toward secure and dependable storage services in cloud computing. IEEE Transactions on Services Computing, 5(2), 220–232).
16. Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592.
17. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
18. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
19. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
20. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
21. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
22. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
23. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
24. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
25. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
26. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
27. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
28. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
29. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
30. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
31. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
32. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
33. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
34. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
35. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
36. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
37. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
38. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
39. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
40. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
41. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
42. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926