ENHANCING DATA PRIVACY IN CLOUD STORAGE: A THREE-LAYER FRAMEWORK WITH FOG COMPUTING

Abstract

In the recent years, there have been notable advancements in cloud computing technology, driven by the escalating volume of unstructured data. This has resulted in heightened attention and improved development of cloud storage technology. Despite these advancements, the current storage approach entails the complete storage of user data on cloud servers, relinquishing users' control and exposing them to privacy risks. Commonly, traditional privacy protection methods rely on encryption technology; however, these approaches often fall short in effectively resisting internal attacks within the cloud server. To address this challenge, I suggest a storage framework comprising three layers with a focus on fog computing. This inventive framework efficiently leverages cloud storage while ensuring the Data confidentiality. Furthermore, our algorithm ‘HashSolomon code’ is crafted to partition data into separate segments, facilitating the secure storage of a portion on local machines and fog servers for enhanced privacy protection. Furthermore, leveraging computational intelligence, this algorithm calculates the distribution ratio of data stored in the cloud, fog, and local machines, individually. The proposed scheme's feasibility has been confirmed through theoretical safety analysis and experimental evaluations, establishing it as a robust complement to existing cloud storage schemes.