Privacy Preserved Secure Offloading in the Multi-access Edge Computing Network

Abstract

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.