Researched On Lightweight Location Privacy Protection Algorithms For Internet Of Things

With the rapid development of GPS-equipped smart mobile devices and mobile computing,location-based services(LBS)are increasing in popularity in the Internet of Things(IoT).Although LBS provide enormous benefits to users,they inevitably introduce some significant privacy concerns.To protect user privacy,a variety of location privacy-preserving schemes have been recently proposed.Among these schemes,the dummy location privacy-preserving(DLP)scheme is a widely used approach to achieve location privacy for mobile users.However,the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices.Moreover,the DLP scheme is inadequate to resist against an adversary with side information.Thus,how to effectively select a dummy location is still a challenge.In order to solve the problems,in this dissertation,we present location privacy protection schemes against snapshot LBS query and continuous LBS query for the Internet of Things services.In the snapshot LBS query service,a lightweight enhanced dummy location privacy protection algorithm called Enhanced-DLP algorithm is presented,which full of considers the side information that an adversary may obtain to choose reasonable dummy locations,to build an anonymous set to protect the user's location privacy.In the analysis of the algorithm time complexity,the time complexity of the algorithm is low when selecting the dummy locations,and it could adapt to the practical requirements of resource-constrained IoT devices.In a continuous LBS query service,a spatiotemporal context privacy protection algorithm that resists side information which adversary obtained is presented.In order to make the adversary insensitive to the side information,it adds noise to the side information of candidate dummy locations by Laplace mechanism which meet the differential privacy requirements.And the user mobile model is used to guarantee the filtrated reasonable dummy locations meet time and space rationality,combining k anonymity technology to protect the user's location information.In order to evaluate the effectiveness of the proposed algorithm,security analysis and simulation experiments were performed.By security analysis,the dummy location privacy protection algorithm presented can defend against the adversary' side information,and can effectively protect the user's location privacy.By simulation experiments,it proves the effectiveness of the presented dummy location privacy protection algorithm.In snapshot LBS query,comparing to the existing dummy location selection privacy protection algorithm,the algorithm presented in this dissertation has a low dummy location selection computational time,and effectively resist the attack of side information.In the continuous query LBS service,the algorithm presented in this dissertation can make the adversary insensitive to side information and be efficient when forming dummy location candidate sets.At the same time,the algorithm builds the anonymity set which has enough indistinguishable paths between context sets.Therefore,the two location privacy protection algorithms presented in this dissertation can be applied to resource-constrained IoT LBS services to effectively protect the location privacy of IoT users.