Privacy-preserving task offloading strategy based on deep reinforcement learning
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.
国家科技期刊平台
NSTL
万方数据
维普
