Research On Trajectory Privacy Protection Based On Hidden Markov Model

In the Environment With the development of big data,human-computer interaction has become more and more frequent.Apps for social networking,positioning,navigation and travel emerge in endlessly.The wide application of GPS,Vehicle Networking and various sensors also marks that people have entered the era of mobile interconnection.Location Based Services(LBS)has become the focus and research direction of all walks of life and become an indispensable part of people's lives.When people use these software and equipment to travel and feel the convenience brought by the development of science and technology,they often neglect the security of their personal privacy.When users use LBS-related services,they can upload,share and publish their location information to the server.How to avoid the risk of privacy disclosure through effective means has become an important research direction of personal privacy and location services under big data.Based on this situation,this paper proposes a method for predicting and protecting the trajectory using the hidden Markov model of the double hidden state.In this method,it is divided into two steps:Firstly,The trajectory prediction algorithm DHMTP based on double hidden state hidden Markov model is improved,so that the model can predict the future adjacent sensitive location,and realize the prediction algorithm DHS-HMP for the trajectory and the future adjacent sensitive location.The k-anonymous trajectory privacy protection algorithm HTAP based on the pseudo-track is established by using the neighbor-sensitive position predicted in the first step.The algorithm selects the specific sensitive points that need to be anonymized by the a priori-posterior probability difference of the sensitive points of the predicted trajectory.The corresponding false points are established by four parameters,and the false trajectories are formed,which realizes the trajectory privacy protection based on hidden Markov model.Finally,the experiment proves that the whole method has higher accuracy trajectory prediction effect and can provide good protection for track privacy.