## 2 Related Work
The data were non-resistant to such attacks; the role-aided encryption process was implemented. The novel EMSA was designed with the association of two traditional approaches, which were the Multi-objective Optimization Algorithm (MOA) and the Euclidean L3P Distance Algorithm (ELDA), as well as the Successive Approximation Iterative Proximate Algorithm (SAIPA). Finally, the efficacy of the recommended work was shown using three measures: privacy, fitness, and utility. From the analysis, the enhanced method achieved better privacy in storing the cloud data. [PERSON] et al. [17] considered the Provable Data Possession (PDP) model to ensure data integrity in the cloud sector. This model cannot ensure the privacy of the data since it is vulnerable to Third-Party Auditors (TPAs). It also depends on the process of the Public Key Infrastructure (PKI), for which the certificates become complex to manage. The \"identity-based (IB) PDP\" mechanism was introduced to overcome these limitations. Owing to the protocol, it struggled with the overhead problems, and the flexible and feasible aspects of the proposed system were used to reduce the complexities. Hence, the findings illustrated that the enhanced method achieved impressive results for the storage process. [PERSON] et al. [18] gave a novel scheme to overcome the problem of allocating the storage nodes in a cloud environment. This was accomplished by representing a bipartite graph with the consideration of code blocks and holding thedata. Subsequently, the storage issue was diminished by using the concept of ant colony and greedy optimization, a clustering-aided approach. The experimental results proved that the implemented work obtained better results regarding displacement among the nodes, computational load, bandwidth, and disk space availability. [PERSON] et al. [28] presented a link-based penalized trust management scheme for preemptive measures to secure edge-based Internet of Things networks. They used a trust-based routing scheme that considered the load factor and the path history of the nodes to balance the traffic and avoid Denial-Of-Service (DoS) attacks. The benefit of this method is that it can improve network performance and reliability by selecting the most-trustworthy and least-vulnerable nodes for data communication. The limitation of this method is the potential for increased complexity in managing distributed systems and the need for efficient resource management, and it may require high coordination between the edge devices and the cloud server. While [PERSON] et al. in [29] discussed the need to enhance the traditional Advanced Encryption Standard (AES) algorithm to cope with emerging security threats in the cloud environment. The authors presented a framework with key features including enhanced security and the owner's data privacy. The authors also introduced a trust and resource management scheme to improve the security and efficiency of the cloud services. The proposed framework modified the 128 AES algorithm to increase the speed of the encryption process, 1000 blocks per second, by the double-round key feature. The proposed algorithm involved less power consumption, better load balancing, and enhanced trust and resource management on the network. The simulation results showed that the proposed framework minimized the energy consumption by 14.43%, the network usage by 11.53%, and the delay by 15.67%. [PERSON] and [PERSON] [19] explored the modified random Fibonacci cryptographic (MRFC) technique using the encryption process with group key management, which resolved the storage problem in the cloud. Initially, the raw data were taken from standard sources. Further, the collected data were distinguished into non-sensitive and sensitive data groups. The sensitive and non-sensitive groups were mixed together for uploading in a private cloud for the encryption process.