Research On Trajectory Privacy Protection Scheme Based On K-anonymity

In intelligent transportation services,whether it is to provide decision support services for government departments and enterprises,or to provide services for individual users,it is inseparable from the mining and analysis of trajectory data.Trajectory data not only contains rich temporal and spatial information,but also includes personal privacy information related to user trajectory.Mining user trajectory data may lead to the disclosure of user privacy.Therefore,how to ensure the availability of trajectory data on the premise of ensuring the privacy of user trajectory has become an urgent problem to be solved.Aiming at different application scenarios of intelligent transportation service,this paper proposes a series of trajectory privacy protection schemes based on k-anonymity.The specific research work and main achievements are as follows:(1)Aiming at the road network planning scenario of intelligent transportation service,unreasonable location points in the data set lead to the low availability of track data,the trajectory privacy protection mechanism based on salp-like swarm algorithm is proposed.Firstly,the construction algorithm of starting point set and ending point set is designed to protect the safety of users’ starting point and ending point.Secondly,a location selection algorithm based on road network is proposed.Considering the current position and trajectory direction,the location of the next moment is selected based on the real road network.Finally,the k-anonymous trajectory set construction algorithm based on salp-like swarm algorithm is used to generate k-1false trajectory from the starting point to the ending point,which improves the trajectory similarity.The experimental results show that the proposed mechanism can effectively reduce the probability of trajectory privacy disclosure and ensure the availability of trajectory data.(2)In the location sharing scenario of intelligent transportation service,there is a problem of trajectory privacy leakage caused by untrusted nodes provided by malicious attackers,and the trajectory privacy protection strategy of collaborative based on user credibility is proposed.Firstly,the user credibility evaluation algorithm is designed,and the trusted users are selected to cooperate and the trusted trajectory set is obtained.Secondly,a sub-trajectory set construction algorithm for time division is designed to cluster the trusted trajectory sets.Each sub-trajectory set after division is consistent in time and is not easy to be identified by attackers.Finally,the trajectory clustering algorithm is used to cluster the similar trajectory in each sub-trajectory set,and the k-anonymous trajectory set is obtained.The experimental results show that the proposed strategy can improve the data utilization while ensuring the privacy of the trajectory.(3)Aiming at the check-in service scenario in intelligent transportation service,the trajectory privacy leakage problem caused by the social attribute of trajectory data is ignored,and the privacy protection strategy of k-anonymous trajectory based on multidimensional collaboration is proposed.Firstly,a social location point selection algorithm is proposed to select the user’s social location.Secondly,a spatial-time-social three-dimensional influence algorithm is designed to calculate the influence of neighboring users on users in three dimensions respectively.Finally,the K-anonymous construction algorithm of trajectory is proposed.The k-anonymous set is constructed by selecting k-1 influential adjacent user locations at the same time together with the real location,and the whole trajectory is anonymous based on sliding window.The experimental results show that the proposed strategy can effectively reduce the probability of successful trajectory attack.In summary,aiming at the problems existing in different scenarios of intelligent transportation service,this paper proposes a trajectory privacy protection scheme based on k-anonymity,which can protect the privacy and security of users’ trajectory and improve the availability of trajectory data.