Abstract
The necessity of security in the cloud system increases day by day in which the data controllers harvest the rising personal and sensitive data volume.The cloud has some unprotected private data as well as data that has been outsourced for public access, which is crucial for cloud security statements. An advanced legal data protection constraint is required due to the resultant of repeated data violations. While dealing with sensitive data, most of the existing techniques failed to handle optimal privacy and different studies were performed to take on cloud privacy preservation. Hence, the novel model of privacy preservation in the cloud and artificial intelligence (AI) techniques were used to tackle these challenges. These AI methods are insight-driven, strategic, and more efficient organizations in cloud computing. However, the cost savings, agility, higher flexibility businesses are offered with cloud computing by data hosting. Data cleansing and restoration are the two major steps involved in the proposed privacy replica. In this study, we proposed Chaotic chemotaxis and Gaussian mutation-based Bacterial Foraging Optimization with genetic crossover operation (CGBFO- GC) algorithm for optimal key generation. Deriving the multi-objective function parameters namely data preservation ratio, hiding ratio, and modification degree that accomplishes optimal key generation using CGBFO- GC algorithm. Ultimately, the proposed CGBFO- GC algorithm provides more efficient performance results in terms of cloud security than an existing method such as SAS-DPSO, CDNNCS, J-SSO, and GC.
A Survey of Cloud Computing and Cloud Security