Research On Privacy And Security Preserving Wireless Offloading In Multi-access Edge Computing

Multi-access Edge Computing(MEC)is introduced as a new computing paradigm to improve information transmission capability in order to meet the increasing computing performance requirements of mobile devices.Wireless offloading is an important technology to realize edge computing.Due to the openness of wireless channel,wireless offloading faces the threat of wireless eavesdropping which poses a serious threat to data security.On the other hand,due to the vaporous geographical distribution characteristics of edge nodes,malicious nodes can collude with each other and estimate location and pattern of users according to the proportion of offloaded tasks to different nodes.These problems greatly limit the practical value of MEC.Therefore,security and privacy protection for wireless offloading is one of the research hotspots in recent years.The thesis considers wireless eavesdropping and privacy leakage problems in MEC.Corresponding solutions including reinforcement learning,genetic algorithm and convex optimization are studied.They are described as follows:(1)Research on j ointly resource optimization of privacy protection and secure transmission based on reinforcement learningAiming at the offloading scenarios that can be offloaded or processed locally,a privacy security protection algorithm based on reinforcement learning Q-Learning is proposed,which can optimize the average energy consumption of MEC system offloading on the premise of guaranteeing privacy and transmission security.Based on the task arrival quantity and wireless channel gain of the Markov decision process modeling system,the influence of key parameters such as privacy measure value and weight of fake task on offloading performance is described.Simulation analysis shows that compared with random offloading strategy,the proposed scheme has lower energy consumption performance.With the increasement of fake task weight coefficient,the privacy level increases,and the proposed scheme’s energy consumption and offloading rate are always lower than that of random offloading strategy.(2)Research on methods of enhanced privacy protection based on task segmentation and message authenticationFirstly,in order to solve the problem of user mode privacy leakage caused by eavesdropper and malicious node theft of task types in edge computing,a privacy security offloading strategy based on task segmentation and message authentication is proposed.Secondly,the privacy level of the offloaded tasks is measured based on the information entropy of the privacy information source,and the segmentation scheme of the offloaded tasks is selected by using the genetic algorithm.For eavesdropping of unknown channel information,the Rayleigh distribution is used to simulate its channel to determine the safe transmission rate under the given interruption probability.Finally,through simulation analysis,it is found that compared with random offloading strategy,the proposed scheme has lower average relative entropy,and compared with the time complexity O(2^N)of backtracking traversal offloading algorithm,the time complexity of the proposed scheme is O(N).(3)Research on optimization methods of security and privacy protection resources for cooperative and parallel offloading of heterogeneous networksAiming at the scenario of multi-user and multi-node in edge computing network,a scheme of mobile devices offloading to multi-node parallelly is proposed to make full use of the computing power of idle nodes to speed up task processing delay and improve user experience.The task entropy of each mobile device is used as the constraint,and the offloading rate is controlled according to the channel characteristics of the eavesdropper with unknown channel information.The problem is reduced to a non-convex problem with constraints,and the continuous convex approximation method is used to solve the problem iteratively.Simulation analysis shows that compared with the non-cooperative scheme,the proposed parallelly offloading computing paradigm obviously has better energy consumption and delay performance,and the advantages are more obvious in the scenario with a higher ratio of nodes to devices.