【1】Yu Yan, Tao Jing, Qinghe Gao, Yingzhen Wu, Xiaoxuan Wang, "Physical Layer Security Enabled Two-Stage AP Selection for Computation Offloading" ,IEEE Global Communications Conference (GLOBECOM) 2022.（CCF C类，北交大A类）

Abstract: Physical layer security (PLS) has been widely employed in studies of computation offloading under traditional centralized networks. Distinguished from existing studies of only combating passive eavesdropper, we propose an efficient user-centric secure two-stage (UCSTS) access point (AP) selection method to combat simultaneously active and passive eavesdropping, which is exploiting the distributed feature of cell-free massive multiple-input-multiple-output (MIMO) scenarios. Furthermore, we propose a novel secure computation task offloading (SCTO) model to guarantee the security of both uplink and downlink transmission.
Aiming at reducing total energy consumption with high security, a minimum energy consumption optimization problem is solved by alternative optimization (AO) algorithm. Simulation results show that the proposed model can well combat Eve while reducing the total energy consumption, and the security of the proposed selection method is better than the AN-based scheme.

【2】Yingzhen Wu, Yan Huo, Qinghe Gao, Yue Wu, Xuehan Li, "Game-theoretic and Learning-aided Physical Layer Security for Multiple intelligent Eavesdroppers",IEEE Global Communications Conference (GLOBECOM) 2022. （CCF C类，北交大A类）

Abstract: Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence of collusive eavesdroppers, who may perceive the environment and move purposefully for better overhearing performance. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and a zero-sum game is exploited to formulate the confrontation with them. The Nash equilibrium is derived by convex optimization and alternative optimization in a specific situation. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium.

【3】Xuehan Li, Tao Jing, Ruinian Li, ,Xiaoxuan Wang, Hengyu Yu, Yan Huo, "On Pursuit of Privacy Preservation for Dependable Offloading in VECON: An Optimization Perspective", IEEEGlobal Communications Conference (GLOBECOM) 2022.（CCF C类，北交大A类）

Abstract: The Internet of Vehicles (IoVs) networks are gaining significant advantages as a result of the emergence of Vehicular Edge Computing and Networks (VECONs). For VECONs, efficient and secure task offloading is essential for users to obtain dependable services. Nevertheless, few efforts are directed towards security issues such as privacy exposure risk during offloading, which seriously affect modeling and optimization of offloading dependability. This paper presents a new measure of offloading dependability entitled cost from Energy consumption, Delay and Privacy exposure risk (cEDP). A dependable offloading framework is proposed, in which tasks are reasonably divided and the Fully Homomorphic Encryption (FHE) algorithm is implemented to mitigate privacy exposure risks. A self-learning based decentralized computation and privacy preservation offloading (SDCPO) algorithm is presented to solve the optimal offloading problem dependably and decentrally. Numerical results show that over the centralized offloading schemes, SDCPO has better convergence performance with lower cEDP, which verifies the dependability of the offloading scheme and algorithm.