INTELLIGENT PRIVACY-PRESERVING AUDITING FRAMEWORK FOR SECURE DATA STORAGE ACROSS FOG AND CLOUD ENVIRONMENTS

Abstract

With the rapid expansion of cloud and fog computing, ensuring secure data storage and integrity has become a major concern for distributed systems. Traditional auditing mechanisms often fail to address the challenges of latency, privacy, and computational efficiency in multi-layered environments. This research proposes an intelligent privacy-preserving auditing framework designed to ensure data integrity and confidentiality across fog and cloud infrastructures. The proposed model integrates lightweight cryptographic primitives with homomorphic authentication to enable secure verification without revealing data contents. It also employs a blockchain-based ledger to achieve tamper-resistant record-keeping for audit operations. Experimental evaluation demonstrates that the framework significantly improves efficiency, scalability, and privacy when compared to conventional cloud auditing methods, making it a robust solution for modern decentralized ecosystems.