Privacy-Aware Offloading in Mobile-Edge Computing

Recently, mobile-edge computing (MEC) emerges as a promising paradigm to enable computation-intensive and delay-sensitive applications at resource limited mobile devices by allowing them to offload their heavy computation tasks to nearby MEC servers through wireless communications. A substantial body of literature is devoted to developing efficient scheduling algorithms that can adapt to the dynamics of both the system and the ambient wireless environments. However, the influence of these task offloading schemes to the mobile users' privacy is largely ignored. In this work, two potential privacy issues induced by the wireless task offloading feature of MEC, location privacy and usage pattern privacy, are identified. To address these two privacy issues, a constrained Markov decision process (CMDP) based privacy-aware task offloading scheduling algorithm is proposed, which allows the mobile device to achieve the best possible delay and energy consumption performance while maintain a pre-specified level of privacy. Numerical results are presented to corroborate the effectiveness of the proposed algorithm.