Research On Location Privacy Protect Algorithm Based On The Semantic Quantification Of Location

With the popularity of the Internet and smart mobile devices,location-based services(LBS)have also been more widely used and have become an important part of people's daily lives.The use of LBS requires users to provide corresponding location information or request information.Untrusted location service providers or other attackers collect and analyze this information to cause user privacy leakage.Therefore,it is particularly important to realize user location privacy protection under the premise of ensuring LBS service quality.Attackers with background knowledge perform inference attacks on location data are one of the main reasons for user location privacy leakage.Attackers can use the historical location information,road information,semantic information and other information they have mastered to carry out reasoning attack on LBS requests submitted by users,thus damaging the protection effect of location privacy.Existing location privacy protection algorithms have been deeply researched in terms of encryption,randomization,obfuscation,etc.,but there are few studies on privacy protection of location semantic information,especially the way to quantify location semantics.In order to solve the above problems,this paper proposes a corresponding location privacy protection algorithm based on the idea of the semantic quantification of location in two cases of snapshot query and continuous query.The main work is as follows:Firstly,in view of the problem that the existing location privacy protection algorithms under snapshot query lack of research on location semantic quantization,this paper proposes a false location generation algorithm based on location semantic quantization on the basis of the existing research on the basis of the existing research.This paper divides the experimental area and constructs semantic location for each location unit by using the user access frequency of different location units in different time periods.Then the candidate false locations with similar probabilities of historical queries are selected.In the process of false position selection,different weight construction methods were used in odd and even rounds to ensure the semantic similarity and the uniformity of physical dispersion of the false position set,and the false position set meeting the user's privacy requirements was generated.Secondly,in view of the problem that the existing location privacy protection algorithm under continuous query cannot consider the rationality of location,location semantic information and location transfer situation at the same time,this paper proposes a location privacy protection algorithm based on multi-objective optimization under continuous query.By analyzing the reasonableness of the candidate false position set,including time rationality,direction similarity,and position semantic similarity,the attacker cannot identify the unreasonable position under continuous query;then,the position weights are calculated for the candidate false positions that pass the rationality detection,and the false positions with better transition probability and physical distribution are randomly selected according to the weight,so as to generate the false position set that can resist the attackers with background knowledge under continuous query.Thirdly,based on two sets of real public data sets,different parameters are set to analyze the performance of the two scenarios under snapshot query and continuous query.Experimental results show that the proposed algorithm can effectively resist the attackers with background knowledge.Compared with the enhanced dummy location selection(enhanced-DLS)algorithm,the proposed algorithm in the snapshot query improves the average anonymity time,physical distribution uniformity and anonymity success rate by 20.5%,45.6% and 12.7% respectively in the Geolife dataset.On the T-Drive dataset,the increase was 34.2%,55.5% and 9.8%,respectively.In the case of continuous query,compared with the robust dummy generation(RDG)algorithm,the position rationality and physical uniformity of the proposed algorithm in the Geolife data set are improved by 49.23% and 7.33% respectively.In the T-Drive dataset,they increased by 49.1%and 6.6%,respectively.