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Research On Privacy Protection Of Continuous Location-Based Services In The Mobile Internet

Posted on:2020-08-07Degree:MasterType:Thesis
Country:ChinaCandidate:J NiuFull Text:PDF
GTID:2428330602950705Subject:Information security
Abstract/Summary:PDF Full Text Request
Recently,with the flourishing of the intelligent mobile devices and the rapidly development of location technologies,on the one hand,effectively prompt various Location-Based Services(LBSs),and on the other hand significantly develop and enhance people's lifestyles and living levels.However,due to the LBS server needs users' sensitive information such as exact locations and query contents,the LBS server may extremely threaten users' privacy when provides all kinds of conveniences and comforts.Meanwhile,the privacy preserving issues of various applications about LBSs have attracted attentions with the constantly improvement of our government on the importance of users' privacy in various applications and the continuously enhancement of the people on the sense of privacy.Therefore,we should pay more attention to users' privacy in LBSs and deal with them effectively.Although there are a great number of existing privacy protection schemes for the issues of LBSs,most of them aim at snapshot queries and rarely involve continuous queries,which are more common in our daily life.However,continuous LBSs easily suffer from location association attacks and trajectory attacks for spatiotemporal correlations between queries and locations.If we utilize the privacy preserving schemes pointed to snapshot queries to continuous queries directly,we won't protect users' privacy or will even leakage users' sensitive information in some extreme cases.At present,there are two approaches to protect users' privacy in continuous location,such as location k-anonymity and dummy-based,but pure location k-anonymity approaches can cause some problems to reveal users' privacy,such as anonymous areas are too large or too small,reduce the efficiency of anonymity and may not protect all users' privacy simultaneously except suffer from those two attacks above-mentioned,or dummy-based approaches are still likely to undergo trajectory attacks even if they have considered spatiotemporal correlations between locations.In this paper,we propose two privacy protection schemes for continuous LBSs,a forecast-based user collaboration scheme and a weak spatiotemporal correlations scheme,to against location association attacks and trajectory attacks,and to improve weaknesses of existing privacy preserving approaches in continuous queries.Our main contributions are summarized as follows:1.After studying and researching privacy protection issues of continuous Location-Based Services(LBSs)in the mobile Internet,we first understand and confirm the service characteristics about continuous LBSs and two main attacks,such as location association attacks and trajectory attacks,which users also inflict in continuous queries,and then we define our research topics.Moreover,we clearly know the research background and challenges of privacy protections in continuous queries,through selecting and analyzing existing privacy protection frameworks and various methods,and comparing advantages and shortcomings among many concrete privacy preserving schemes for continuous LBSs,which provides solid theoretical and practical foundations for us to propose new privacy protection schemes for continuous LBSs.2.We propose a forecast-based user collaboration privacy protection scheme for continuous LBSs to against location association attacks,trajectory attacks and privacy leakage issues caused by too large or too small anonymous areas.The method proposed main utilizes a framework of distributed-based user collaboration,which can avoid effectively point attacks of the Trusted Third Party(TTP)attached to central-based systems.In our scheme,the query initiator first utilize all collaborated users' current information and predict all collaborated users' query information in the following time by combining some factors such as area overlapping,time reachability,trend similarity and out-degree/in-degree of locations.Then the initiator judge the reasonableness and usability of the predicted information according to a prediction return checking mechanism.Finally,the initiator achieve location k-anonymity and query L-level.On the one hand,this approach ensure the usability of the predicted information and the accuracy of the query results,and it also achieves all participant users' privacy protections,moreover it can against location association attacks and trajectory attacks simultaneously.On the other hand,it not only can effectively avoid the situations of anonymous areas too large or too small in processes of continuous queries,but can reduce the rebuilting numbers of user-collaborated groups,increase the query efficiency and deduce communication costs largely.3.We propose a weak spatiotemporal correlations privacy preserving scheme for continuous LBSs to solve the privacy leakage issues of existing dummy-based methods in the Mobile Internet,such as ignoring of time factors,the unreasonableness filtering requirements of dummies and not consider the factors of the Time-Sensitive Side Information(TSSI),spatiotemporal correlations and movement patterns of previous locations simultaneously.In our paper,the query initiator first considers the Time-Sensitive Side Information(TSSI),and generates dummies by the existing dummy-generated approaches,such as the method of enhanced-DLS.Then he sets four dummy filtering requirements,namely area overlapping,time reachability,trend similarity and out-degree/in-degree,to filter out dummies according to analyzing spatiotemporal correlations between locations and the movement patterns of previous locations.Finally,the initiator achieves location k-anonymity.This scheme not only increases the usabilities and passing possibilities of dummies by considering time factor when generating dummies at first,and it involves fine-grained time factors and ensures the accuracy of dummies' query frequency.But due to consider area overlapping in the following time,the movement patterns in previous neighbor queries and the number of location out-degree/in-degree,so the dummy filtering requirements are more reasonableness and it can cut off the spatiotemporal correlations between locations effectively,solve the privacy leakage problems caused by the shortest number of out-degree/in-degree requirements in existing methods and defense location association attacks and trajectory attacks simultaneously.4.We first analyze and evaluate the privacy,safety and usability of our proposed two schemes in theory.Then we confirm the feasibility of these two schemes by carrying out extensive experiments in real dataset such as Geolife Trajectories 13.The experimental results show that,on the one hand,our proposed schemes protect all participant collaborated users' privacy in continuous LBSs.One the other hand,it can against location association attacks and trajectory attacks simultaneously on the basis of increasing the passing probability of dummies.Therefore,our proposed schemes can effectively protect users' privacy of continuous Location-Based Services in the Mobile Internet.
Keywords/Search Tags:Location-Based Services(LBSs), location privacy, continuous query, spatiotemporal correlations, user collaboration, dummies, forecast-based
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