Privacy Preservation of Healthcare Data in Hybrid Cloud using a Hybrid Meta-Heuristics Based Sanitization Technique

Over the recent years, the expansion of cloud computing services enable hospitals and institutions to transit their healthcare data to the cloud, thus it provides the worldwide data access and on-demand high quality services at a cheaper rate. Despite the benefits of healthcare cloud services, the associated privacy issues are widely concerned by individuals and governments. Privacy risks rise when outsourcing personal healthcare records to cloud due to the sensitive nature of health information and the social and legal implications for its disclosure. Over the recent years, a privacy-preserving data mining (PPDM) technique has become a critical issue for the problems. Our goal is to design a privacy-preserving outsourcing framework under the hybrid cloud model. In this work we propose a Hybrid Ant Colony Optimization and Gravitational Search Algorithm (ACOGSA) to express the problem of hiding sensitive data through transaction deletion. Thus, it reduces the side effects of the hybrid cloud. Substantive experiments will be carried to compare the performance of the designed algorithm with the state-of-the-art approaches in terms of the side effects and database similarity (integrity). Over the past to sanitize the databases used for hiding sensitive information, a few heuristic approaches have been proposed. The method used for the comparison involves GA, PSO, ACO, and Firefly framework.