Research Of K-anonymity Privacy Preserving Based On Clustering

With the rapid development of mobile Internet in recent years,intelligent mobile terminal plays an indispensable role in people's daily life.The location based service,which is derived from the combination of positioning technology and geographic information system,has greatly facilitated the way people travel and the content of entertainment.However,the premise of using location-based services is to send personal location information to LBS providers,followed with the security of location privacy.Therefore,it is necessary to use technical means to ensure the security of the user's location privacy when using the LBS.While protecting location privacy,we also need to pay attention to the quality of query service.Balancing the two aspects is also a concern for the study of location privacy protection.The location privacy protection technology and the use of the existing system structure have been studied widely and deeply.Besides,this paper analyzes the advantages and disadvantages of the existing system structures and algorithms.By learning this paper proposes a k-anonymity location privacy protection model based on clustering.This model constructs classification of anonymous groups by cluster theory.Compared with the traditional k-anonymity methods,this algorithm improves the quality of service with the condition of ensuring users' location privacy.The main work of this paper is follows:(1)a detailed study of the existing location privacy preserving technology and the system structure are introduced and the advantages and disadvantages of each kind of location privacy protection technology are compared;we understand the key issues of location privacy protection algorithm to solve and lay the foundation for the following research.(2)This paper proposes the use of density clustering to realize the anonymous grouping process in the anonymous privacy of location,which makes the distribution of users in the anonymous group relatively more reasonable under the condition of ensuring privacy security.(3)In the process of clustering and grouping,the concept of outliers is introduced.After the initial anonymous grouping is completed,the anonymous group is continuously adjusted.In this process,the influence of outliers existing in the anonymous group on the grouping is eliminated,and the anonymous position data is further weakened.(4)Using the location data generated by road network data generator Thomas Brinkhoff to simulate the algorithm,and compared with the classical algorithms in terms of anonymous success rate,anonymous processing time and query accuracy.The experimental results prove the effectiveness of the method.