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With the widespread use of location-based services (LBS), the number of trajectories gathered by location service providers is dynamically growing. On the one hand, mining and analyzing these spatiotemporal trajectories can help to work out a mobile-related strategic planning; on the other hand, knowledge of each trajectory can be used by adversaries to identify the user's sensitive information and lead to an unpredictable harm. The concept of trajectory k-anonymity extends from location k-anonymity that has been widely used to address this issue. The main challenge of trajectory k-anonymity is the selection of a trajectory k-anonymity set. However, existing anonymity methods ignore the trajectory similarity and direction, assuming that it has little impact on privacy. Thus, it cannot provide a preferable trajectory k-anonymity set. In this paper, we propose to use trajectory angle to evaluate trajectory similarity and …