Journal of ISMAC
IRO Journals
Volume 4 — Issue 2 — June 2022

Review on Advanced Cost Effective Approach for Privacy with Dataset in Cloud Storage

https://doi.org/10.36548/jismac.2022.2.001
Pages: 73-83
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Vijesh Joe
Data Scientist, WoTo Technologies
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How to Cite

Joe, Vijesh. 2022. “Review on Advanced Cost Effective Approach for Privacy With Dataset in Cloud Storage”. Journal of ISMAC 4 (2): 73-83. https://doi.org/10.36548/jismac.2022.2.001.
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Keywords

— Privacy in cloud
— cloud storage
— authentication
— data security
— data protection
— homomorphic encryption
Published: 13-07-2022

Abstract

Cloud computing allows customers to run compute and data-intensive applications without the need for a large investment in infrastructure. Additionally, a significant amount of intermediate datasets are created and often saved, in order to reduce the expense of re-computing these applications. It becomes difficult to protect the privacy of intermediate datasets because attackers may be able to retrieve information that is sensitive to privacy via the analysis of several intermediate datasets. Existing techniques to deal with this problem generally endorse the use of encryption for all cloud datasets. For data-intensive applications, the time and expense of repeatedly decrypting and encrypting intermediate datasets are prohibitive; hence, encrypting all intermediate datasets does not make sense. Big heterogeneous data storage concerns and challenges, countermeasures (security and administration) and cloud storage prospects, are discussed in this article. New questions arise for cloud storage researchers, when they examine these issues in depth.

References

  1. Maximilian Wöhrer, Uwe Zdun, “ Smart contracts: Security patterns in the ethereum ecosystem and solidity”, International Workshop on Blockchain Oriented Software Engineering (IWBOSE) ,IEEE, 2018.
  2. Qiwu Zou, Yuzhe Tang, Ju Chen, Kai Li, Charles A. Kamhoua, Kevin Kwiat, Laurent Njilla, “ChainFS: Blockchain-Secured Cloud Storage”, IEEE 11th International Conference on Cloud Computing (CLOUD), 2018.
  3. Deepika Saxena and Ashutosh Kumar Singh. workload forecasting and resource management models based on machine learning for cloud computing environments. arXiv preprint arXiv:2106.15112, 2021.
  4. Jitendra Kumar, Ashutosh Kumar Singh, and Rajkumar Buyya. Ensemble learning based predictive framework for virtual machine resource request prediction. Neurocomputing, 397:20–30, 2020.
  5. Sakshi Chhabra and Ashutosh Kumar Singh. Dynamic data leakage detection model based approach for map reduces computational security in cloud. In 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS), pages 13–19. IEEE, 2016.
  6. AK Singh and Jitendra Kumar. Secure and energy aware load balancing framework for cloud data centre networks. Electronics Letters, 55(9):540–541, 2019.
  7. Aman Singh Chauhan, Dikshika Rani, Akash Kumar, Rishabh Gupta, and Ashutosh Kumar Singh. A survey on privacy-preserving outsourced data on cloud with multiple data providers. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC), 2020.
  8. Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi, and Rebecca N Wright. Privacy-preserving machine learning as a service. Proc. Priv. Enhancing Technol., 2018(3):123–142, 2018.
  9. Ping Li, Jin Li, Zhengan Huang, Chong-Zhi Gao, Wen-Bin Chen, and Kai Chen. Privacy-preserving outsourced classification in cloud computing. Cluster Computing, 21(1):277–286, 2018.
  10. Xindi Ma, Jianfeng Ma, Hui Li, Qi Jiang, and Sheng Gao. Pdlm: Privacy-preserving deep learning model on cloud with multiple keys. IEEE Transactions on Services Computing, 2018.
  11. B. Mao, S. Wu, and H. Jiang, “Exploiting workload characteristics and service diversity to improve the availability of cloud storage systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 7, pp. 2010–2021, July 2016.
  12. M. H. Chen, Y. C. Tung, S. H. Hung, K. C. J. Lin, and C. F. Chou, “Availability is not enough: Minimizing joint response time in peer-assisted cloud storage systems,” IEEE Systems Journal, vol. PP, no. 99, pp. 1–11, 2016.
  13. Cigref, “cloud computing and protection of data”, in publication of network companies enterprises 2015.
  14. A. Petit , Ben Mokhtar, L. Brunie and H. Kosch, “Towards Efficient and Accurate Privacy Preserving Web Search,” MW4NG ’14 December 8-12, 2014, Bordeaux, France. 2014.
  15. X. Zhang, C. Liu, S. pandey and J. Chen, “A Privacy Leakage Upper Bound Constraint Based Approach for Cost-Effective Privacy preserving of Intermediate Data Sets in Cloud,” IEEE, Vol 24, No. 6, June 2013.
  16. T. Praveena, G Raja, “Threshold Based Filtering approach For Cost Effective over Encrypted Cloud Data ", IJISET, Vol. 1 Issue 3, May 2014.
  17. Ms. C. Celcia, Mrs. T. Kavitha, “Privacy Preserving Heuristic Approach fpr Intermediate Data Sets in Cloud,” IJETT, Vol 9, March 2014 ISSN 2231-5381.
  18. K. Zhang, X. Zhou, Y. Chen, X. Wang, Y. Ruan, “Sedic: Privacy Aware Data intensive Computing on Hybrid Clouds,” Proc. 18th ACM Conf. Computer and Comm. Security (CCS”11), pp 515-526, 2011.
  19. Y. Hua, B. Xiao, X. Liu, and D. Feng, “The design and implementations of locality-aware approximate queries in hybrid storage systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 11, pp. 3194–3207, Nov 2015.
  20. C. Lalit, I. Majitar, and B. Rabindranath, “A comprehensive survey on internet of things (iot) toward 5g wireless systems,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16 – 32, 2020.
  21. P. Samuela, C. Claudia, B. Marina, T. Marco, P. Valeria, G. Andrea, and F. Manuel, “Iot enabling technologies for extreme connectivity smart grid applications,” in CTTE–FITCE: Smart Cities & Information and Communication Technology (CTTE–FITCE). IEEE, 2020, pp. 16–32.
  22. X. Shuming, N. Qiang,W. Liangmin, andW. Qian, “Sem-acsit: Secure and efficient multi-authority access control for iot cloud storage,” IEEE Internet of Things Journal, pp. 1 – 1, 2020.
  23. G. Tudor, C.-C. Andrei, A.-C. Madalina, and Z. Alexandru, “Cloud storage a comparison between centralized solutions versus decentralized cloud storage solutions using blockchain technology,” in 54th International Universities Power Engineering Conference (UPEC). IEEE, 2019, pp. 16 – 32.
  24. B. Mao, S. Wu, and H. Jiang, “Exploiting workload characteristics and service diversity to improve the availability of cloud storage systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 7, pp. 2010–2021, July 2016.
  25. M. H. Chen, Y. C. Tung, S. H. Hung, K. C. J. Lin, and C. F. Chou, “Availability is not enough: Minimizing joint response time in peer-assisted cloud storage systems,” IEEE Systems Journal, vol. PP, no. 99, pp. 1–11, 2016.
  26. D. Pritam and M. Chatterjee, “Enforcing role-based access control for secure data storage in cloud using authentication and encryption techniques,” Journal of Network Communications and Emerging Technologies (JNCET), vol. 6, no. 4, 2016.