Decoy Security for Chronical data in Fog Environment

Abstract

Fog and cloud computing systems are now widely used as a result of the rising need for chronic big data processing and analysis. Yet, because of the existence of multiple privacy concerns, maintaining the privacy of Chronical large data in these situations continues to be difficult. This study examines a paradigm for managing chronic big data in cloud and fog environments while maintaining privacy. The suggested framework is intended to solve the shortcomings of current privacy-preserving techniques and offer a scalable and practical privacy-preservation solution. For data collecting, storage, processing, and analysis, the framework consists of a number of components. Data encryption, access control, and data anonymization approaches all preserve user data privacy. The outcomes showed that the proposed architecture was effective in preserving the confidentiality of Chronical large data in fog and cloud settings. Performance of the system was assessed using metrics like privacy preservation, scalability, and computational efficiency. With the potential to be expanded to other industries including the internet of things (IoT), financial services, and e-commerce, this work makes a significant addition to the field of privacy preservation in fog and cloud environments.