The analysis of user trajectory information and social relationships in social media, combined with the personalization of travel needs, allows users to better plan their travel routes. However, existing methods take only local factors into account, which results in a lack of pertinence and accuracy for the recommended route. In this study, we propose a method by which user clustering, improved genetic, and rectangular region path planning algorithms are combined to design personalized travel routes for users. First, the social relationships of users are analyzed, and close friends are clustered into categories to obtain several friend clusters. Next, the historical trajectory data of users in the cluster are analyzed to obtain joint points in the trajectory map, these are matched according to the keywords entered by users. Finally, the search area is narrowed and the recommended travel route is obtained through improved genetic and rectangular region path planning algorithms. Theoretical analyses and experimental evaluations show that the proposed method is more accurate at path prediction and regional coverage than other methods. In particular, the average area coverage rate of the proposed method is better than that of the existing algorithm, with a maximum increasement ratio of 31.80% .

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.