Research On Location K Anonymity Privacy Protection Scheme Resist Inference Attacks

Currently,the popularity of various location-based services(LBS)has greatly facilitated users'travel and social activities.However,due to untrusted LBS application service providers or malicious attackers will use various privacy mining methods to analyze the user's various location privacy information from the query requests submitted by users,it may lead to the disclosure of the user's location privacy or the illegal use of privacy data.This allows users to enjoying the convenience of using LBS application services while also facing serious privacy threats.Users'concerns about location privacy have become one of the main bottlenecks hindering the healthy development of the LBS application market.Therefore,the research and design of the location privacy protection method that can protect the user's location privacy while providing users with high-quality services when using the LBS application services has important practical significance for the user and the LBS application market.Due to the simple and practical characteristics of location k anonymity technology,it has been adopted by many location privacy protection schemes.However,the existing location privacy protection methods based on location k anonymity technology focus on how to improve the degree of location privacy protection,but the quality of service and the degree of location privacy protection cannot be well balanced;in addition,there are also problems that cannot meet the user's more personalized location privacy protection needs,and the resource overhead is large.In view of the above problems,this thesis introduces the definition of service similarity,quantifies the attacker background knowledge,and conducts research in combination with the location entropy measurement mechanism,and proposes a location privacy protection scheme that effectively solves the above problems.The main research work of this thesis is as follows:(1)This thesis analyzes and compares the existing location k anonymity privacy protection methods,and points out the shortcomings of the existing methods.Through in-depth investigation and research,this thesis studies and designs specific and effective solutions.(2)This thesis proposes a service similarity location k anonymity privacy protection scheme resist inference attacks.This method introduces service similarity and quantifies the background information of the historical query probability that the attacker may have.By integrating the mechanism of improving service ava ilability based on similarity and the privacy protection optimization mechanism based on location entropy to resist the background information inference attacks,this method achieves a better trade-off between the user's location service quality and location privacy protection,and can effectively resist the attacker background information inference attacks.Through the corresponding performance verification experiments,the performance superiority of the method is verified.(3)This thesis proposes a new scheme for personalized location k anonymous privacy protection based on service similarity.This method provides users with input-type privacy protection measurement parameters and quality of service measurement parameters,and provides parameter selection comparison table including the numerical relationship among privacy protection measurement parameter k,quality of service measurement parameterS_d and anonymous success rate,which can provide users with more personalized location privacy protection and has lower resource overhead.The corresponding performance comparison experiments verifies the performance superiority of this method in four aspects:location privacy protection,location service quality,time overhead and communication overhead.