Sanitization and restoration of under-utilized healthcare database in single-cloud and multi-cloud environment using optimal key generation for personalized privacy preservation

The growth of cloud computing technologies in modern ages has enabled institutions as well as hospitals to move its healthcare data to cloud and as a result, it enables global data right of entry and on-demand superior services at a lower cost. Governments and consumers are concerned about the privacy dangers that come with healthcare cloud services, governments and consumers are worried about the privacy risks that come with them. Because of the delicate nature of health data in addition to the legal and social repercussions of its revelation, outsourcing personal healthcare archives to the cloud increases privacy concerns. Recently Privacy-Preserving Data Mining (PPDM) techniques had a several severe issues for this problem. Thereby, this work intends to introduce an optimization based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under 2 processes namely; "restoration and data sanitization". The sensitivity of the data is masked while sanitization procedure, preventing sensitivity of the data from leaking on cloud side. Additionally, both routine and sensitive data is kept in a cloud setting. For the sanitization procedure, a key must be created using the Modified Grey Wolf Optimization (MGWO) method, which is a novel optimization technique. The identical key must be utilized to restore the original data efficiently during data restoration. The optimum key making is prepared in such a manner that the goal model including privacy, sanitization effectiveness, and restoration effectiveness is successfully enhanced in the cloud environment, which successfully boosts the conservation of healthcare-data. Finally, the superiority of the projected strategy over other existing methods is confirmed in terms of several metrics such as privacy and so on.