Performance Analysis on Optimal Data Integrity and Security-Constrained Cloud Storage Framework with Meta-heuristic-Based Mechanism

The cloud computation applications need a novel and effective framework to balance the security of their data. In cloud computing systems, security issues are considered becomes complicated, where the data storage is situated in various locations in the universe. Cloud users worry about the main security constraints of cloud computing are data confidentiality and data integrity. Therefore, this problem mentioned above in cloud computing is motivated for developing an efficient and secured cloud storage system by utilizing the optimization algorithm of the Pathfinder Algorithm (PFA). Further, the data are gathered from the benchmark data set. Then, the collected data is fed into the optimal sensitive attribute selection stage along with the PFA application. Further, the optimal sensitive attributes are secured with the help of Optimal Minimum-Storage Regenerating Code (OM-SRC), where parameter optimization occurs with the PFA. The remaining non-sensitive attributes are secured with the utilization of the Shuffling Algorithm. Finally, the attributes like sensitive and non-sensitive are fused and uploaded into the cloud environment. The storage mechanism efficiency is tested and correlated with the other comparative algorithms. Finally, an effective examination of the suggested technique is done to show the effectiveness of changing the parameters.