Privacy Protection In The Context Of Location-based Services

Location-based services(LBS)combine the functionality of location-aware devices(e.g.,GPS-like devices),wireless communication technologies,and information management to provide personalized services for users based on their current locations.Examples of LBS include location-aware emergency services,location-based advertisement,live traffic reports,and location-based store finders.The user registered with LBS continuously sends his/her location to a location-based database server.Upon requesting LBS,the registered user issues a location-based query that is executed at the server based on the knowledge of the user's current location.Although LBS promise safety and convenience,they threaten the privacy and security of their users.The privacy threat comes from the fact that LBS providers rely mainly on an implicit assumption that the user agrees to reveal his/her private location to get LBS.In other words,the user trades his/her privacy with the service.If the user wants to keep his/her private location information,the user has to turn off the location-aware device and(temporarily)unsubscribe from the service.With potentially untrusted servers,such a service subscription model poses several privacy threats to the user.In many real-life cases,people abuse GPS devices to stalk personal locations,and many people worry about their location privacy when they are using LBS.Unfortunately,the traditional approach of pseudonymity(i.e.,using a fake identity)is not applicable to LBS,as the location information of a person can directly lead to the true identity.For example,asking about the nearest Pizza restaurant to the location of his house using a fake identity will reveal my true identity,as a resident of the house.In fact,many web-based tools are available to translate a location into a street address and the resident of a street address.Our thesis is that the quality of the location-based services critically depends on the performance of the service,the privacy assurances offered to the clients,and the quality of the data provided by the service.In this dissertation,we propose three contributions addressing key aspects of these challenges:network performance,location privacy for the mobile clients,and relevance of data provided by the service.Firstly,the explosive growth of smart phones with GPS results in a number of innovative applications.However,while location-based services bring people the convenience of life,it also makes the personal's privacy disclosure.For protecting location privacy of user,we proposed a spatio-temporal location privacy preserving algorithm based on nearest neighbor search.First,we design a data structure to support our algorithm,and use nearest neighbor search to achieve it.Second,when achieving location k-anonymity,we not only consider the level of location privacy parameter k and cloaking area Amin but also consider the sensitive region user located in.Third,extensive experiments are conducted,and experimental results show that our proposed algorithm outperforms prior cloaking algorithms.Secondly,due to the different requirements of each individual for their location privacy,there are some methods to deal with location privacy disclosure.However,these methods still have imperfections.Therefore,in this paper,privacy requirements of obfuscation position range for suitable everyone is proposed.We first obfuscate the user's real position by their location privacy requirements.Then shift the obfuscation area with a random angle and constrains distance.The random angle assure the attacker cannot infer which direction the obfuscation is,and a constrained distance assure the attacker cannot infer whether the individual in privacy region.Finally,we give some experiment to test our methods.The experimental effects show that our methods are effective.Thirdly,location-based services require users to supply their location information.However,a user's location can be tracked without her consent or knowledge.Once the trajectory of a user is identied,his locations will be exposed.Thus,it is critical to protect the movement trajectories of mobile users in order to preserve user location privacy.In this paper,we propose to use dummies to achieve privacy protection in the P2P context.First,the user according to their position generate two dummy in order to form a triangle,with the center of gravity of the triangle as a protected area center,in order to generate the rest of the dummy;Second,we designed a dummy generated algorithm in client and a server response method;Finally,we validate experiments and make theoretical analysis of the experimental results.Finally,the traditional privacy protection model of location-based services would lead to the disclosure of user privacy when applied directly to the road network environment,and existing privacy preserving of road network model do not considering the density of the user and the road length.To solve this problem,we propose a Euclidean distance-based road network privacy protection model,and design algorithms to achieve it.First,we put the projection distance of sections in the road network context as the actual distance between two users located in this section,and for the users located in deference.For users in different sections,we put the sum of distance with respect to the length of sections as the relative position between them;Secondly,combining traditional anonymity models and road network characteristics,we propose the road network(k,l,?)-anonymous model and design algorithms to implement it;Finally,the experimental verification performance of the algorithm implementation.Theoretical analysis and experimental results show that the algorithm is correct and effective.