Research On Location Privacy Protection Model Based On Differential Privac

With the rapid development of global positioning system and wireless communication network,location-based services have become an indispensable part of daily life,involving all aspects of life.However,while providing convenience for people’s life,it also increases the security risks of location data.However,due to the large volume of location data,most existing location privacy protection protocols are unable to take into account the utility and privacy of data,and cannot resist attacks based on background knowledge,and lack of reasonable measurement of privacy protection level.And provide quality service to the location data mining analysis,the direct analysis of location data or malicious mining will expose the location data of sensitive information,therefore,when service providers in the release position data must be for data preprocessing,in ensuring the availability to release results of the degree of privacy protection at the same time,strengthen the location data.In view of the above problems,this thesis adopts differential privacy,location entropy,K-means clustering and other technologies to study location privacy protection from the following two aspects:1)Location data privacy protection protocol based on differential privacy.When a user initiates a location query request,the current exact location may be disclosed.If an attacker illegally obtains and exploits the exact location,the user will be troubled.In order to solve this problem,an optimal user-assisted selection algorithm for constructing anonymous set was designed based on location entropy,and the user’s reputation was evaluated by smart contract.A differential privacy protection protocol based on location entropy was proposed,and the query results were optimized.The simulation results show that the model can resist background knowledge attack and realize controllable privacy protection.Ensure data availability in effectively protecting location data privacy.2)K-means location data publishing method based on differential privacy.The research shows that the location data publishing method based on differential privacy and K-means is more suitable for the case of uneven data distribution and small query scope.Difference of privacy as a powerful mathematical theory support of privacy framework,applied to the position of the release of the data privacy protection method,through the study of the division of spatial data,repass K-means clustering for data processing,and then in a way to meet the difference of privacy protection to split after each subdomain added noise.To ensure that individual information is not compromised when responding to range queries.However,due to the data distribution characteristics and the addition of noise,there is some error in the query results,which affects the data availability.In order to verify the feasibility of the proposed scheme,theoretical analysis and experiments prove that the model can effectively protect the privacy of location data publishing results and ensure the availability of publishing results.