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Protecting Your Offloading Preference: Privacy-aware Online Computation Offloading in Mobile Blockchain

The high computational capacity demanded in blockchain mining hinders the involvement of mobile devices due to their limited computation power. Offloading the blockchain mining task to base stations (BSs) is a promising solution for mobile blockchain. Recently, many reinforcement learning (RL)-based approaches achieve the long-term performance of Quality of Service (QoS) during online computation offloading, but they fail to consider the risk of privacy leakage. Existing works for privacy preserving share an unresolved problem that they provide a private mechanism by adding private constraints into the value function of RL algorithm but neglect to protect the value function itself. Hence, we investigate a novel privacy issue caused by value function leakage, named offloading preference leakage. To solve this issue, we propose a privacy-aware deep RL method (PA-DRL) for computation offloading over the mobile blockchain. Specifically, a functional noise is generated, then added to the exploring and policy updating processes of DRL. Furthermore, we adopt a cooperative exploring mechanism and prioritized experience replay (PER) to improve the convergence rate of the proposed method. We provide the theoretical analysis for privacy preserving and convergence. Finally, simulation results show that our method can perform cost-efficient computation offloading, compared with benchmark methods.