Hybrid heuristic-based key generation protocol for intelligent privacy preservation in cloud sector

Abstract

Cloud Computing is defined as a set of software and hardware that are used together for delivering different kinds of cloud services based on user's demand. Cloud computing secures a major role in the Information Technology (IT) industry for accessing the services at any place around the world. On the other hand, there are increasing vulnerabilities and threats in the cloud environment due to the rise in popularity and demands in cloud computing services. Data privacy and integrity are the major issues in cloud computing while storing data in various geographical locations. So, it is necessary to consider the data privacy and integrity factors in a cloud computing environment. It is difficult to construct a common platform to interact in the cloud environment. So, it is essential for implementing the security solutions, which need to provide confidentiality while exchanging the data. The main intention of this paper is to design and develop a novel artificial intelligence approach for handling the privacy preservation problem in the cloud sector. Through the process of data sanitization, the sensitive data is hidden, so that it cannot be accessed by unauthorized users. To perform the sanitization process, the heuristic-based key generations play a vital role, and here, it is solved by considering a multi-objective function with constraints like the “degree of modification, hiding ratio, and information preservation ratio”. This multi-objective problem is solved by the adoption of novel Probability Switch searched Butterfly-Moth Flame Optimization (PS-BMFO). Finally, the restoration is also performed by the same PS-BMFO-based key generation. The analysis results confirm that the suggested model preserves privacy and ensures the integrity of the user's data against unauthorized parties. From the experimental analysis, the proposed PS-BMFO gives 12.5%, 10%, 30.7%, and 12.5% enriched than GWO, JA, MFO, and BOA, respectively. Therefore, the statistical analysis shows that the developed data privacy preservation model with suggested PS-BMFO performs better than the other conventional algorithms.