Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

中文翻译：

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移动边缘计算的隐私感知在线任务卸载

移动边缘计算 (MEC) 被视为第五代 (5G) 移动网络中最有前途的技术之一。它允许移动设备将其计算要求高和延迟关键的任务卸载到资源丰富的 MEC 服务器。因此，MEC 可以显着提高移动设备的延迟性能并降低能耗。尽管如此，在任务卸载过程中可能会发生隐私泄漏。大多数现有工作都忽略了这些问题，或者只是研究了 MEC 的系统级解决方案。隐私感知和用户级任务卸载优化问题很少受到关注。为了应对这些挑战，小号，本文提出了一种用于 MEC 的隐私保护和设备管理的任务卸载方案。该方案可以在保护用户的位置隐私和使用模式隐私的同时，实现接近最优的延迟和能源性能。首先，我们将任务卸载和隐私保护的联合优化问题表述为半参数上下文多臂老虎机 (MAB) 问题，该问题具有宽松的奖励模型。然后，我们提出了一种基于转换后的汤普森采样 (TS) 架构的隐私感知在线任务卸载 (PAOTO) 算法，通过该算法我们可以 (1) 获得最佳的延迟和能耗性能，(2) 实现目标保护隐私，以及 (3) 无需任何系统级信息即可获得在线设备管理的任务卸载策略。