An improved DDPG-based privacy sensitive level protection computation offloading method in mobile edge computing

Abstract

With the development of fifth-generation mobile communication technology and artificial intelligence, resource-intensive applications on mobile terminal devices process tasks by offloading them to servers. However, the presence of untrustworthy edge servers poses a threat to user terminal (UT) privacy. Therefore, this paper proposes an efficient computation offloading architecture based on personalized privacy protection to address the privacy leakage problem during computation offloading. In this architecture, we design a personalized privacy-sensitive level calculation method to achieve hierarchical protection of personal information. This method calculates based on users’ demands for four indicators: integrity, confidentiality, availability, and real-time performance of tasks at different times and locations. An improved deep deterministic policy gradient (DDPG) method is proposed to resolve the optimization solution. The method solves the overestimation problem in the original DDPG algorithm and utilizes an optimal behavior selection mechanism with privacy-preserving constraints. Simulation experiments show that the proposed scheme increases the weighted sum of latency and energy consumption by an average of 29.32%, while effectively protecting user privacy.