The Research On The Privacy Protection Mechanism Of Mobile User Location In Edge Computing Environment

With the development of Io T and the popularity of mobile smart devices(such as mobile phones,connected cars,bracelets,etc.),the application scenarios of edge computing are gradually diversifying and user data is being transferred more frequently.Users are required to provide their own information(such as location,speed,POI,etc.)in most location-based services(LBS),and attackers may obtain or tamper with the information during transmission,which brings about problems such as user privacy data leakage.To address these issues,privacy protection for mobile users in edge environments has become a hot research topic.However,existing privacy protection techniques often affect the quality of LBS services by disturbing users’ information,thus degrading the user experience.In addition,edge computing cannot be separated from task allocation and task offloading,and there are still problems such as unreasonable resource allocation and high task offloading delay in the current edge computing applications.How to obtain the optimal average latency through joint optimization of task scheduling and resource allocation is also a topic that cannot be ignored.In response to the above background and challenges,this paper proposes two approaches based on location services in edge computing environments,as follows:1.A privacy protection and service evaluation method for location services based on edge computing environment is proposed.By building a location service privacy protection and system evaluation system in the edge computing environment,we build a data processing module and a service evaluation module based on existing work on protecting user privacy,and design an evaluation algorithm,in which an evaluation model NPE and an evaluation model POE are designed based on research related to POI recommendations.specifically,in the evaluation model NPE we treat each user decision as a set of factors and provide a method for learning factor embedding.In the evaluation model POE,we learn about the hidden intent of the user’s next action by combining different factors such as metadata information and two temporal contexts in a unified way.Experiments are also designed to demonstrate that this method can improve service quality while protecting user privacy.2.A method for optimising encryption mechanisms and resource allocation based on edge computing environments is proposed.In order to protect user information during task offloading,a local differential privacy algorithm based on histogram algorithm is used so that it can accurately retain user contextual information while reducing interference with reproduction decisions.To efficiently offload tasks and improve offloading efficiency,a joint optimisation algorithm for task offloading and resource allocation that can optimise global delays is proposed.A balance is found between the degree of privacy data protection and the accuracy of task offloading.The impact of perturbing contextual information on task offloading decisions is minimised while ensuring a predetermined degree of privacy data protection.Using a specific connected vehicle as an example,the method can offload tasks to roadside units and neighbouring connected vehicles with sufficient computational resources.The method is also evaluated through simulation experiments to verify the feasibility and effectiveness of the method.