曲阜师范大学学报（自然科学版）2024，Vol.50Issue(1)：8-15,8.DOI:10.3969/j.issn.1001-5337.2024.1.008
基于深度强化学习的隐私保护任务卸载策略
Privacy-preserving task offloading strategy based on deep reinforcement learning
摘要
Abstract
Existing deep reinforcement learning-based computational offloading approaches protect only usage pattern privacy and location privacy.In this paper,we consider a new privacy problem in multi-MEC server networks,i.e.,computational offloading task feature privacy leakage,which is lacking in current re-search.To address this issue,this paper proposes a new DQN-based privacy-preserving online computation-al offloading approach in MEC networks.The method will set the privacy leakage threshold by measuring the privacy leakage of the computational offloading feature task,which can protect the privacy information of user offloading;then the optimization problem is transformed into a Markov decision process with the objective of minimizing system energy consumption;finally the optimal offloading decision satisfying the privacy constraint and minimizing energy consumption objective is solved by the DQN-based algorithm.The experimental results show that the algorithm can effectively reduce the total energy consumption of the system while effectively reducing user privacy leakage.关键词
任务卸载/多MEC服务器/隐私泄漏量/DQNKey words
task offloading/multi-MEC server/privacy leakage/DQN分类
计算机与自动化引用本文复制引用
王亚林,王康,张博文,王茂励..基于深度强化学习的隐私保护任务卸载策略[J].曲阜师范大学学报（自然科学版）,2024,50(1):8-15,8.基金项目
山东省自然科学基金(ZR20211260301). （ZR20211260301）
