Differential and Access Policy Based Privacy-Preserving Model in Cloud Environment

Abstract

Cloud computing has multiple benefits in terms of minimum cost, maximum efficiency, and high scalability, which prompts shifting a large amount of data from the local machine to the cloud environment for storage, computation, and data sharing among various parties stakeholders. However, owners do not fully trust the cloud platform operated by a third party. Therefore, security and privacy emerge as critical issues while sharing data among different parties. In this paper, a novel privacy-preserving model is proposed by utilizing encryption, differential privacy, and machine learning approaches. It facilitates data owners to share their data securely in the cloud environment. The model defines access policy and communication protocol among the involved untrusted parties for data processing and privacy preservation. The proposed model is evaluated by executing experiments using distinct datasets. The achieved results reveal that the proposed model provides high accuracy, precision, recall, and f1-score up to 98%, 98%, 97%, and 97%, respectively, over the state of the art methods.