Research On Privacy Protection Technology In Mobile Edge Computing Environment

With the rapid development of mobile internet technology,mobile terminals are widely used in people’s daily lives,and the issue of privacy data leakage stored internally has attracted people’s attention.Therefore,in the mobile edge computing(MEC)environment,the significance of studying privacy protection technology is very important.At present,there are two main issues regarding privacy protection for mobile terminals:(1)Traditional indoor location privacy protection has the problems of user privacy data leakage,long positioning time,and difficulty in resisting multiple attacks;(2)The data transmission brought about by task offloading of mobile terminal devices mostly relies on wireless communication technology,making the privacy information of mobile terminal devices easy to be eavesdropped and monitored.In response to the above issues,this thesis conducted the following research on privacy protection technology in the MEC environment:(1)A federated learning based indoor location privacy protection method in MEC environment has been designed.This method first designs an indoor location privacy protection model,which uses kanonymity and differential privacy to generalize and add noise to the collected location dataset.The deterministic indistinguishability of k-anonymity is combined with the random indistinguishability of alpha differential privacy to generate an anonymous location dataset.Then establish an indoor location federated learning model and design an indoor location dataset privacy protection algorithm(ILD-PPA algorithm).Experimental results have shown that this method improves the ability to protect user location privacy and reduces localization time.(2)A privacy protection method based on task offloading algorithm in MEC environment has been designed.This method comprehensively considers the location privacy and association privacy issues of mobile devices during task offloading in the MEC environment.Firstly,a privacy protection model and a time model were designed,where the privacy protection model includes location privacy and association privacy.Then,using MSS proxy forwarding technology,a location and association privacy protection algorithm based on task offloading algorithm(TOA-LAPPA algorithm)was designed.Firstly,tasks were classified,and then two priority,proxy,and edge server selection strategies were designed to minimize the average task completion time while protecting location and association privacy.Experimental results have shown that this method improves the ability to protect location and association privacy,and reduces the average task completion time.