Privacy Based Data Publishing Model for Cloud Computing Environment

Abstract

Cloud computing is a popular model for providing data storage services from remote computing facilities through internet. Security is known as an element for protecting sensitive information from vulnerable attacks and ensuring information confidentiality, integrity and authenticity. Privacy is the assurance that users could maintain complete control over their sensitive information. Cloud storage-based data publication is significant in medical field where it contains sensitive information such as nature of the disease, patient medical history, and effects of the illness. The publisher should not disclose any of the individual or sensitive information of the individuals with the research board while publishing the reports to the medical data analysts. Deciding on the nature of sensitivity, the user may be allowed to access the information from cloud environment that is a complex process. In order to ensure the complete privacy of individual medical history, the present research work employs k-anonymization to upgrade the privacy policies in the cloud storage. In addition to this, the genetic grey wolf optimization algorithm is employed to decide the data to be published based on the information preserved for privacy purposes. The proposed work is evaluated in a real cloud infrastructure with respect to privacy, utility and information losses. The results show that the proposed method is efficient for privacy-based data publication as it conceals the sensitive information effectively.