Securing Mobile Cloud Data Offloading in Blockchain Using Deep Deterministic Policy Gradient with Game Theory

In the era of ubiquitous connectivity and the proliferation of mobile devices, the demand for efficient and secure data offloading to the cloud has become paramount. This paper presents a novel framework that supports deep reinforcement learning and blockchain technology to enable secure and efficient mobile cloud data offloading. Addressing the challenge of optimizing data offloading decisions in mobile cloud environments by implementing a Deep Deterministic Policy Gradient algorithm (DDPG) to determine the optimal data offloading strategy. To incentivize user participation, introducing a game-theoretic reward mechanism that considers user contributions based on the offloading decisions made by the DDPG agent. This ensures a fair and efficient allocation of resources while maximizing overall system utility. Furthermore, integrating blockchain technology to ensure the security and integrity of the offloaded data's. By recording transactions on an immutable and transparent ledger, to provide a robust mechanism for data provenance, access control, and tamper-proof storage. The experimental results demonstrate the efficacy of the proposed framework in determining optimal strategies for data offloading decisions, enhancing user participation, and ensuring data security in mobile cloud environments. The evaluation of the system and the security analysis revealed advancements in the resource-efficient access control architecture, which attained reduced network latency while also delivering robust security and data privacy compared to existing data sharing models.