电讯技术2025，Vol.65Issue(10)：1595-1605,11.DOI:10.20079/j.issn.1001-893x.240325002
任务卸载位置隐私保护的强化学习算法
Reinforcement Learning Algorithm for Task Offloading with Location Privacy Protection
摘要
Abstract
For the problem of location privacy leakage in edge computing task offloading,position K-anonymous technology is used for task offloading of intelligent terminal equipment to generate K-anonymous area,and terminal equipment task is offloaded through all terminal equipment in K-anonymous area,to protect the location privacy of terminal devices.A reinforcement learning based location privacy protection mechanism for task offloading(RL-LPTO)is proposed,which deploys Actor and Critic networks to optimize task offloading decisions while preserving location privacy.A dual-part Actor network structure is designed on each terminal device to enable both task forwarding and offloading decisions,facilitating agent training and optimizing latency and energy consumption.Simulation results show that the RL-LPTO algorithm reduces the performance cost of task offloading to 55%of the average cost of baseline algorithms,while effectively protecting location privacy.关键词
边缘计算/位置隐私/K-匿名/任务卸载/强化学习Key words
edge computing/location privacy/K-anonymity/task offloading/reinforcement learning分类
信息技术与安全科学引用本文复制引用
张鉴,韩言妮,安伟,陶涛,王学建,范东媛..任务卸载位置隐私保护的强化学习算法[J].电讯技术,2025,65(10):1595-1605,11.基金项目
重庆市教育委员会在渝本科高校与中国科学院战略课题(HZ2021015) （HZ2021015）
