The Study Of Privacy Preservation In Location-based Services

In recent years,with the rapid development of mobile Internet technology and information technology,mobile intelligent terminals have become an indispensable part of people's lives,providing a variety of personalized services.The existing mobile intelligent terminals are generally equipped with GPS positioning function,which utilizes mobile user's location to require diverse services from various applications.Therefore,location-based services(LBSs)become the one of the most popular service in the mobile Internet,providing users with various service information,which enhances mobile users' lives.However,while users enjoy the convenience brought by location-based services,they need to send their personal privacy to the corresponding location-based service provider,in order to exchange service information.Mobile user's personal privacy includes location information,query content,and query range.At the same time,LBS provider collects mobile user's requirements,and then they analyze and infer mobile user's privacy,such as mobile user's identity,interests,occupation,health situation.If the LBS provider's server is compromised by a malicious user or a malicious server,there is no privacy issues existed for mobile user.The leakage of privacy leads to loss of user interests and even personal safety issues.Therefore,in LBS,protecting users' private information has become an indispensable part of the development of mobile Internet.In this dissertation,we analyze the privacy problems in existing privacy-preserving schemes.In order to solve these problems,we consider five aspects to achieve privacy protection,such as,service quality,time sensitization,spatio-temporal correlation,attribute awareness,and querying range.In LBSs,the proposed solutions guarantee mobile user's service quality and privacy preservation simultaneously.The main research contents of this dissertation are as follows:(1)In existing privacy-protecting schemes,they focus on protectting mobile user's privacy,and neglect the obtaining service quality.We propose a privacy strategy,which considers service quality enjoying by mobile user.In this privacy strategy,we propose two privacy metrics,including dual-privacy metric and service quality metric.Dual-privacy metric measures mobile user's location privacy and query privacy.Furthermore,based on the existing side information,we define a new concept,fine-grained side information,which is the necessary part of the proposed privacy-preserving frame.(2)In LBS,the most privacy-protecting schemes consider the querying probability of a particular location in history,but ignore the possibility of sending requirement at different timestamp.Therefore,we define a new concept called time-sensitive side information,and design a privacy-protecting strategy considering the effect of time factor.Based on the analyzing of privacy-protecting strategy,we aim to protect mobile user's location privacy and query privacy.(3)According to the spatio-temporal correlation,we propose a dummy-based privacy-protecting scheme,which consists of two algorithms,map division algorithm and dummy-requirement selecting algorithm.In map division algorithm,we divide the map into discrete location cells,which ensures the selected discrete location cells are not adjacent to each other.We infer the possible location that mobile user will go to at the next timestamp,which is employed to design the query content at current timestamp.Therefore,we avoid inference attack,according to spatio-temporal correlation.(4)As one of the most significant factors for privacy protection,side information has been considered in designing privacy-preserving schemes in LBSs over recent years.However,most existing schemes consider this concept through a straightforward way,such as query probability.In this paper,we consider the basic attribute associating with each location and design an attribute-aware privacy-preserving scheme(APS)to enhance mobile user's location privacy.Specifically,we first extract basic attributes from the local map,and specialize the attribute-aware side information(AASI).Then we build an attribute-based hierarchical tree(A-tree),which classifies locations into different categories in term of each location's attribute.Based on such information,we design APS,which consists of two algorithms,Voronoi dividing algorithm(VDA)and dummy determining algorithm(DDA).In VDA,we divide the local map into different Voronoi polygons based on the properties of Voronoi diagram,which guarantees the selected locations are dispersed.In DDA,we utilize the four color map theorem to color these Voronoi polygons,which helps mobile users to choose the dummy locations as far as possible.Therefore,our APS provides an optimal dummy set to protect mobile user's location privacy and query privacy.(5)In mobile social networks,in order to find the interesting poi nearby,mobile users have to submit querying range to LBS server.However,the existing dummy-based privacypreserving mechanisms,aiming to generate dummy locations and ignoring the querying range.We propose a personalized range-sensitive privacy-preserving scheme,which includes three algorithms,map storing algorithm,personalized range-estimating algorithm and requirements generating algorithm.In map storing algorithm,we employ binary quadtree to reduce storage cost,enhancing searching efficiency.In personalized range-estimating algorithm and requirements generating algorithm,each dummy location equipped with a personalized range,consisting the submitted requirements.(6)Finally,we conclude our contributions and explain our future works.