Privacy-Aware Offloading Strategy via Self-Supervised Feature Mapping in the End-Edge-Cloud System
Rui Zhang, Xuemei Zhao, Yajing Li, Shipu Zheng, Ruhui Ma, Mengke Tian, Youhua Xue, Yong Wang, Haibing Guan · ACM Transactions on Sensor Networks · 2024
In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a solution, where task offloading plays a crucial role in efficiently allocating computing resources. Meanwhile, driven by the growing social awareness of privacy, privacy-aware task offloading methods have attracted significant attention. However, existing privacy-aware task offloading methods face various limitations, such as being applicable to specific scenarios, poor transfer ability of offloading strategies, etc . This paper studies the privacy-aware task offloading problem in the end-edge-cloud system and proposes PATO , a P rivacy- A ware T ask O ffloading strategy. PATO consists of two core modules. Specifically, a novel self-supervised feature mapping module transforms sensitive information via complex unidirectional mapping. Subsequently, a DRL-based decision-making module is trained to utilize transformed information to make task offloading decisions. Subtly combining the self-supervised feature mapping module and the DRL-based decision-making module, the proposed PATO addresses both privacy protection and task offloading challenges. Furthermore, PATO is designed as a general solution for task offloading problems and exhibits good transfer ability.