Privacy-Preserving Task Offloading Strategies in MEC

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-12, Vol.23 (1), p.95
• Main Authors: Yu, Haijian, Liu, Jing, Hu, Chunjie, Zhu, Ziqi
• Format: Article
• Language: English
• Subjects: Algorithms; association privacy; Cloud computing; Communication; Completion time; Computation offloading; Computers, Handheld; Data transfer (computers); Design; Eavesdropping; Energy consumption; Experiments; Information Technology; Leakage; location privacy; Mobile devices; mobile edge computing; Policy; Privacy; privacy protection; Risk; Servers; task offloading; Wireless communications; Wireless telecommunications equipment
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010095

In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.