An IoT-Centric Data Protection Method for Preserving Security and Privacy in Cloud

Abstract

A malevolent utility provider may extract outsourced data from the cloud while storing, analyzing, and sharing the data among the involved entities to acquire sensitive information that can be misused. Therefore, data protection has become a challenging task that needs to be tackled appropriately. To address this pivotal and challenging issue, this article proposes a secure data protection method for preserving privacy in the cloud environment by effectively partitioning, partially decrypting, and analyzing the data that improves the model's efficiency while maintaining security. The model ensures the system's security and privacy by performing secure data storage, analysis, and sharing. The numerous experiments are conducted and achieved results demonstrate that the proposed method procures data privacy with high accuracy, precision, recall, and F1-score up to 96.85%, 96.65%, 96.85%, and 96.72% with a relative improvement up to 45.58%, 49.31%, 45.58%, and 48.46%, respectively, for varieties of datasets as compared to state-of-the-art works.