A k-Anonymity Completion Method Generated Based on Semantic Fusion Trajectories
Trajectory privacy protection is one of the hot issues in the field of data security and personal privacy protection. Aiming at the problem that the number of anonymous trajectories might be insufficient in k-anonymous trajectory computation,the article proposed an anonymous trajectory generation method based on semantic fusion. The method selected pairs of trajectories with spacing less than a specified threshold and with pathways,and generates two virtual trajectories with better semantic interpretations after fusion and calibration. Based on the above research results,the article further proposed an anonymous trajectory set complementation algorithm based on semantic fusion trajectory generation. The method first selected trajectories from the anonymous trajectory set as the candidate trajectory set;then,the eligible trajectory pairs were selected from the candidate trajectory set to execute the semantic fusion-based anonymous trajectory generation method,and the eligible generated trajectories were added into the anonymous trajectory set. If the number of anonymous trajectory sets was still not enough to meet the requirements,suitable trajectories could also be selected again from the trajectories eliminated by the k-anonymous trajectory computation to be added to the candidate trajectory set,and the trajectory fusion generation could be performed again. This step also added the eligible generated trajectories into the anonymous trajectory set again until the number of anonymous trajectory sets reached the requirement. The trajectory generation and anonymous trajectory complementation method proposed in the article not only has good interpretability,but also can effectively solve the problem of insufficient number of trajectories that may be encountered in k-anonymous trajectory computation.
万方数据
维普
