Privacy-Aware Online Task Offloading for Mobile-Edge Computing

Mobile-edge computing (MEC) has great advantages in reducing latency and energy consumption, where mobile devices (MDs) can offload their computation-demanding and latency-critical tasks. However, privacy leakage may occur during the tasks offloading process, and most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and device-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 to achieve a delay and energy sub-optimal solution 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.