A secure and efficient fog-based storage framework using LRC

Recent advancements in cloud computing have raised significant concerns about data privacy and control, particularly against internal threats and vulnerabilities in traditional encryption methods. To address these challenges, a novel three-tier storage framework incorporating fog computing and Locally Repairable Codes (LRCs) is proposed. This advanced model partitions data into fragments using LRC-based encoding, enabling efficient and localized repair mechanisms. The framework distributes these fragments across local machines, fog servers, and cloud storage, optimizing data placement based on sensitivity, access patterns, and storage efficiency. Computational intelligence dynamically adapts redundancy levels and determines the optimal data distribution, ensuring secure, low-latency access while maintaining cost efficiency. LRCs significantly reduce bandwidth and latency during repairs by limiting fragment retrieval to localized groups, enhancing overall system scalability and resilience. In this work, LRCs are extended to a fog–cloud framework by introducing a three-tier data distribution strategy, localized repair mechanisms at fog servers, and dynamic redundancy adaptation based on system conditions. Experimental results demonstrate a 30–40% improvement in storage efficiency, along with robust data privacy and fault tolerance. This innovative approach leverages the strengths of cloud and fog computing, offering a scalable and secure solution for modern data management challenges.