Learning-Based Privacy-Preserving Computation Offloading in Multi-Access Edge Computing

As a technology intended to reduce cellular network congestion and enhance user service quality, computation offloading in Multi-access Edge Computing (MEC) networks highlights the crucial issue of privacy protection. This paper proposes a novel solution to the computation offloading and privacy protection problem in the MEC network using a Multi-agent Deep Deterministic Policy Gradient (MADDPG) framework. Our approach utilizes game theory to encourage computation offloading by modeling the interaction between cloudlets, Data Center Operator (DCO), and users as an auction game. We formulate the resource allocation and privacy protection as an auction game with multiple bidders and incomplete information and then use MADDPG to find an optimal solution. To ensure privacy protection, we design a Local Differential Privacy (LDP) method in the MADDPG algorithm. Theoretical analysis and simulation results demonstrate the effectiveness of our approach in satisfying differential privacy and converging to an equilibrium. The proposed solution holds significant promise in addressing the computation offloading and privacy protection challenges in MEC networks.

I. Introduction

Due to the rapid expansion of mobile devices and network services in fifth-generation (5G) and sixth-generation (6G) networks, Multi-Access Edge Computing (MEC) networks face a significant challenge in catering to the dynamic requirements of mobile users and ensuring the protection of their privacy [1]. This challenge is further compounded by the growing concern among users regarding the potential compromise of their personal information, thus necessitating the prioritization of privacy protection [2]. Consequently, there is an urgent need to improve the processing speed of users' computing tasks, assist them in offloading computing tasks and allocating resources at the edge, and effectively safeguard their privacy [2].