A reformed grasshopper optimization with genetic principle for securing medical data

Abstract

Cloud computing is an emerging computing technology that uses the internet and central remote servers to sustain data and applications. Using cloud computing, users can access database resources through internet from anywhere without distressing about the management of actual resources. This concept can be used almost in all fields including the healthcare sector. In such situations, data mining and their effective investigation from cloud are more demanding due to the privacy in data preservation. Consequently, the privacy and security of the information system is under major risk because of security attacks like Known Plain text attack (KPA) and Cipher Text attack (CPA). To manage these issues, this paper describes privacy preservation approach with data sanitization and data restoration process for securing medical data. Moreover, several researchers have proposed enhancements to the restoration process, since it causes a low accuracy. As a solution to this crisis, this paper uses a hybrid algorithm known as Grasshopper Optimization with Genetic Algorithm (GOAGA) for the data sanitization and restoration process. Further, the performance of this hybrid approach is compared with other conventional approaches in terms of the sanitization and restoration effectiveness, convergence, statistical and key sensitivity analysis and thus the governance of the progressed approach is validated.