Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.

翻译：移动边缘计算（MEC）技术被引入以在网络边缘实现云计算，从而帮助资源受限的移动设备处理时间敏感的数据任务。在该范式中，移动设备可通过无线通信将其计算密集型任务卸载至附近更高效的MEC服务器。因此，该领域的研究主要聚焦于开发高效的卸载方案，而忽视了移动用户的隐私问题。尽管区块链技术被用作安全共享数据的信任机制，但无线通信引发的隐私问题——即使用模式隐私和位置隐私——正是本工作的核心。本文研究了这些隐私关切对任务卸载马尔可夫决策过程（MDP）的影响，并利用深度循环Q网络（DRQN）求解该MDP。数值仿真结果验证了所提方法的有效性。