Research And Implementation Of A Location Privacy Preserving Algorithm In Mobile Crowdsensing

Mobile Crowdsensing is a promising mobile sensing and computing paradigm,which is an extension of wireless sensor network(WSN)under the background of mobile Internet and Internet of things.Mobile Crowdsensing leverages network connected intelligent devices' capabilities of sensing and computing to perform the function of wireless sensor network.Compared to traditional WSN,mobile crowdsensing has wider suitable applicable range,and is able to perform tasks which is difficult before with very low cost.For this reason,it has remarkable prospect and significance for study.Privacy preserving is an important issue in mobile crowdsensing.The objective of privacy preserving is to prevent users' sensitive privacy information exposure when participating sensing activities.Location information sharing problem in privacy preserving is studied in this paper.Through evaluation on location privacy exposure risk locally,users decide whether to share location related data or not.The major research and implementation of this paper are as follows:Quantizing location privacy exposure risk by geometric method which are distance and point set uniformity.This approach ensures users to post complete data including user identification and location and so on.The system utilizes trajectory compressing algorithm to compress users' trajectories in order to reduce time overhead and computation complexity at first.Next,defining location privacy risk standard based on trajectories kept locally and posted records,computing and evaluating privacy exposure to minimize user's trajectories exposure and the risk of sensitive locations privacy reveal on local device to determine whether to share location related data.This study implements the location sharing algorithm of privacy preserving.Simulating algorithm execution process using test data,then receiving privacy exposure degree on sensitive locations and trajectories.Moreover,simulation experiments based on real-world dataset show that the method achieves high performance,scalability,privacy preserving effectiveness,social contribution and better privacy preserving compared to other similar approach,thus,it can successfully deal with tracking,identification and profiling threats from attackers.As a result,this algorithm is able to provide relatively high user location privacy with sharing sensitive information properly.It is a novel privacy preserving solution which is practical and provides high level privacy protection.