The recent rapid growth of cloud service providers as more and more users and organizations are moving towards the multi-cloud systems, so that data can be accessed from any part of the world but it poses a humongous problem related to security and privacy of data. Cloud industry needs robust data security system. This research study investigates the feasibility, challenges, and potential impacts of implementing an automated data deletion system, integrated with the capabilities of intrusion detection, in a multi-cloud environment. Through qualitative methods, the aim to understand the experiences, perspectives, and insights of key stakeholders involved in the deployment and operation of such systems. Data collection methods include surveys of focused groups with cloud security experts, IT managers, compliance officers, and developers and an in-depth analysis of existing models and architectures, internal reports, whitepapers, policy documents, compliance guidelines, and security incident records. This research provides an insight and in-depth understanding of the requirements of the individual users and stakeholders of various organizations and improving the overall efficiency of multi-cloud environments by implementing the proposed Automated Data Deletion System with Intrusion Detection System.

Cloud Computing, Data Privacy, Data Security, Automated Data Deletion System, Intrusion Detection System

[1] Blesson Varghese and Rajkumar Buyya. 2018. Next generation cloud computing. Future Gener. Comput. Syst. 79, P3 (Feb.), pp.849–861, 2018. https://doi.org/10.1016/j.future.2017.09.020
[2] Yang, P., Xiong, N., Ren, J., 2020. Data security and privacy protection for cloud storage: A survey. IEEE Access 8, 131723–131740. Yu, Y. et al., 2016. Identity-based remote data integrity checking with perfect data DOI: https://doi.org/10.1109/ACCESS.2020.3009876
[3] Kavitha, M.G., Radha, D., Quality, Security Issues, and Challenges in Multi-cloud Environment: A Comprehensive Review. In: Nagarajan, R., Raj, P., Thirunavukarasu, R. (eds) Operationalizing Multi-Cloud Environments. EAI/Springer Innovations in Communication and Computing. Springer, Cham. 2022. https://doi.org/10.1007/978-3-030-74402-1_15
[4] Spencer, K., Withana, C., Exploring Cyber Security Challenges of Multi-cloud Environments in the Public Sector. In: Mukhopadhyay, S.C., Senanayake, S.N.A., Withana, P.C. (eds) Innovative Technologies in Intelligent Systems and Industrial Applications. CITISIA 2022. Lecture Notes in Electrical Engineering, Vol.1029, 2023. Springer, Cham. https://doi.org/10.1007/978-3-031-29078-7_19
[5] Morgan Reece, Theodore Edward lander, Matthew stoffolano, Andy Sampson, Josiah Dykstra, Sudip Mittal, Nidhi Rastogi Systemic Risk and Vulnerability Analysis of Multi-cloud Environments, 2023. DOI: https://doi.org/10.48550/arXiv.2306.01862
[6] S. Kanaga Suba Raja, A. Sathya, S. Karthikeyan, and T. Janane Multi cloud-based secure privacy preservation of hospital data in cloud computing, Vol.10, 2021. https://doi.org/10.1504/IJCC.2021.113993
[7] I. Gupta and A. K. Singh, "Dynamic threshold-based information leaker identification scheme", Inf. Process. Lett., Vol.147, pp.69-73, 2019. DOI: https://doi.org/10.1016/j.ipl.2019.03.005
[8] S. Wang, J. Zhou, J. K. Liu, J. Yu, J. Chen, and W. Xie, "An efficient file hierarchy attribute-based encryption scheme in cloud computing", IEEE Trans. Inf. Forensics Security, Vol. 11, No.6, pp.1265-1277, 2016. DOI: https://doi.org/10.1109/TIFS.2016.2523941
[9] Pan Jun Sun Privacy Protection and Data Security in Cloud Computing: A Survey, Challenges, and Solutions, Vol.7, 2019. DOI: 10.1109/ACCESS.2019.2946185
[10] Kire Jakimoski Security Techniques for Data Protection in Cloud Computing International Journal of Grid and Distributed Computing Vol.9, No.1, pp.49-56, 2016. doi: http://dx.doi.org/10.14257/ijgdc.2016.9.1.05
[11] Deepika Saxena, Rishabh Gupta and Ashutosh Kumar Singh A SURVEY AND COMPARATIVE STUDY ON MULTI-CLOUD ARCHITECTURES: EMERGING ISSUES AND CHALLENGES FOR CLOUD FEDERATION arXiv:2108.12831v1 [cs.DC] 29 Aug 2021. DOI: https://doi.org/10.48550/arXiv.2108.12831
[12] Q. Zhang, S. Li, Z. Li, Y. Xing, Z. Yang, and Y. Dai, “Charm: A cost-efficient multi-cloud data hosting scheme with high availability,” IEEE Transactions on Cloud computing, Vol.3, No.3, pp.372–386, 2015. DOI: https://doi.org/10.1109/TPDS.2023.3306150
[13] I. Clouds, “A white paper from the open cloud standards incubator,” Distributed Management Task Force, Version, Vol.1, 2009.
[14] A. Galis, E. Elmroth, W. Emmerich, F. Galán, and S. Telefónica, “The reservoir model and architecture for open federated cloud computing,” IEEE Computer Society Press, Vol.20, pp.115–187, 2009.
[15] E. Fernández-del Castillo, D. Scardaci, and Á. L. García, “The egi federated cloud e-infrastructure,” Procedia Computer Science, Vol.68, pp. 196–205, 2015 DOI: https://doi.org/10.1016/j.procs.2015.09.235
[16] D. Bernstein, “Cloud foundry aims to become the OpenStack of paas,” IEEE Cloud Computing, Vol.1, No.2, pp.57–60, 2014. DOI: https://doi.org/10.14738/tmlai.54.3334
[17] M. E. Moudni and E. Ziyati, "A Multi-Cloud and Zero-Trust based Approach for Secure and Redundant Data Storage," 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), Istanbul, Turkiye, pp.1-6, 2023. doi: 10.1109/WINCOM59760.2023.10323009.
[18] Z. Chen, F. Han, J. Cao, X. Jiang and S. Chen, "Cloud computing-based forensic analysis for collaborative network security management system," in Tsinghua Science and Technology, Vol.18, No.1, pp.40-50, Feb.2013, doi: 10.1109/TST.2013.6449406.
[19] Zhang, X., Cui, L., Shen, W. et al. File processing security detection in multi-cloud environments: a process mining approach. J Cloud Comp 12, 100, 2023. https://doi.org/10.1186/s13677-023-00474-y
[20] Li J, Tong X, Liu J, Cheng L., An efficient federated learning system for network intrusion detection. IEEE Syst J. 17(2):2455-64, 2023. DOI:10.1109/JSYST.2023.3236995
[21] Chiba Z, Abghour N, Moussaid K, Rida M et al., Intelligent approach to build a deep neural network based ids for cloud environment using combination of machine learning algorithms. Comput Secur 86:291–317, 2019. doi: https://doi.org/10.1016/j.cose.2019.06.013