Research On Online User Recruitment Under Bilateral Privacy Protection In Mobile Crowdsensing

In recent years,due to the rapid development of Internet of Things technology and the emergence of more and more mobile smart devices,mobile crowdsensing is gradually emerging as a paradigm that can gather large-scale and distributed mobile users for data collection.Mobile crowdsensing can motivate and recruit large-scale mobile users to perform sensing activities,enabling mobile users to use their own mobile smart devices to perform large-scale data collection tasks,thereby enabling users who participate in sensing activities to form a large-scale mobile the perception network can finally ensure that the platform or task issuer can obtain high-quality perception data.In the past,the traditional data collection method is to deploy sensors in fixed locations or specific areas to collect data.However,the disadvantages of this traditional method are the high cost of equipment deployment,the small scope of the collected data,and the poor quality of the collected data.Mobile crowdsensing can effectively avoid the above problems and achieve good data collection results.Therefore,mobile crowdsensing is now widely used in road congestion control,temperature and humidity detection and other fields.When performing a perception task or uploading perception data after performing a perception task,the user may inadvertently reveal his current location,and the task publisher does not want the location information of the task to be performed to be leaked to other unrelated users.or individual.At the same time,the entire process of collecting data must not only be aggregated to the platform,but also processed and transmitted through many fog nodes,and users may not be able to stay connected to the platform for a long time due to various reasons.Select users to perform perception tasks.Therefore,when the platform recruits users to perform perception tasks,how to protect the privacy information of users and task publishers from being leaked,and how to recruit a suitable group of users to perform perception tasks in an online scenario have become impossible for the platform.key issues to avoid.In order to solve the above problems,this paper conducts in-depth research on the privacy leakage of users and task publishers and how the platform conducts user recruitment in online scenarios,and proposes an online user recruitment method under the protection of bilateral privacy.The details are as follows:When the task publisher publishes the task location information and the user uploads the current location information,a multi-secret sharing method is used to construct a bilateral privacy protection framework.Under this framework,it can be ensured that relevant private information will not be exposed to other unrelated individuals.At the same time,because the platform cannot grasp the information of each user in advance before the user is connected to the platform,and the user may connect to the platform or disconnect the connection at any time.Therefore,the platform needs to immediately decide on whether to choose or not for each user connected to the platform,and needs to give the corresponding salary when the specific utility of selecting the current user to perform the perception task is uncertain.In response to the above problems,this paper simulates the interaction between the platform and users according to the reverse auction framework,and designs a user recruitment method suitable for online scenarios under the bilateral privacy protection framework proposed in this paper.The method consists of two parts,namely the winner selection strategy and the compensation determination strategy.For the above framework and methods,theoretical proofs are given for analysis.Through the simulation experiments on the GPS trajectory data of the three datasets,it can be seen from the experimental results that the method proposed in this paper has a good effect in maximizing the total completion probability of all tasks compared with other baseline methods,while satisfying Authenticity,individual rationality,computational efficiency,and good budget utilization.