Designing Healthcare Data Preservation Model via Genetically Modified Glowworm Swarm Optimization

In general, Cloud computing is a computing prototype that grants energetic accessible frame especially for information, application as well as file-storing. Moreover, the prescribed model is more popular for its variety of beneficial aspects in terms of minimum consumption cost, and due to this characteristic, the technique successfully contributed almost in all areas especially in medical or healthcare sector. So, an effective analysis and extraction of information are imperative since they are facing many challenging issues. Along with this, the information or data that are in communication must be preserved to maintain its privacy needs. Hence, the privacy preservation of data is a great challenge that should be resolved expeditiously. This consideration has necessitated "a privacy-preserving algorithm in both processes: Data sanitization and Data restoration". In this context, it is planned to solve preservation issues by introducing a hybridized model termed as "Genetically-Modified Glowworm Swarm (GMGW) for both data sanitization and data restoration processes". Furthermore, the proposed scheme is evaluated over existing models with respect to certain measures and the supremacy of the proposed scheme is demonstrated.