| Proximity testing is based on the LBS system(Location Based Service),it has the important functions to search for nearby users.This feature is widely used in social,commercial,military and other fields.Nowadays,people are paying more attention to the privacy protection while enjoying the location services.Since the principle of searching for nearby friends in the proximity testing is based on the position of the users,it is easy to disclose the privacy of the location information.So most of the proximity testing algorithms have greatly increased the time complexity while emphasizing the protection of privacy,and then affected the testing efficiency.The purpose of this paper is to improve the efficiency of proximity testing algorithms under the premise of protecting users’ privacy and enhance the practicability of the services.In this paper,we use Euclidean space to simulate the actual situation of spatiotemporal database and to facilitate the theoretical argumentation,and propose the proximity testing algorithm in Euclidean space and highway network model.Also in this paper,the two distance models between Euclidean space and highway network are combined in the ideal environment and practical application,and their respective advantages are brought into full play.In addition,this paper maximizes the protection of users’ location privacy.First,the privacy proximity testing algorithm is proposed by using the features of Euclidean space.The algorithm protects its location privacy by anonymizing the users to be tested.In addition,the test region is segmented by constructing Voronoi unit,and the condition of the possible results set is selected to reduce the test area,Thus,it can improve the test efficiency.After that,the performance of this algorithm and the traditional proximity testing algorithm are compared by the simulation data in terms of the test accuracy and time-consuming.The results show that it is better than the traditional proximity testing algorithm in the test efficiency,and can effectively protect the tested users’ location privacy.Then,this paper proposes an improved algorithm in the forming foundation,which can make up for the complex computation of the Euclidean space.The algorithm is to separate the two-dimensional testing plane by extending the circle.According to the distributions of the tested users in three regions,the algorithm obtains the possible results set.And then it calculates the possible value that the tested users are proximity,At last it compares the probable value with a predetermined threshold.In addition,the performance of the algorithm is compared with that of the Euclidean space proximity testing algorithm.After data testing in the simulation,it is found that the algorithm has better performance in terms of efficiency and precision. |
