Information extracted from trajectory data is very useful in many practical application scenarios. Before trajectories for data mining are published, they need to be processed to protect the privacy of the trajectories' bodies. In this paper, a method for such privacy protection is proposed. Our method guarantees that the generated trajectory points satisfy the k-anonymity by interchanging the positions of the trajectory points on the k-core subnet of the relation network. The method treats the trajectory points as the privacy protection object. It overcomes the curse of dimensionality resulting from the K-anonymity of trajectories, and reduces the distortion of the generated trajectories significantly. Moreover, our proposed strategy can preserve the original positions of the trajectory points. Experiments on both real-life and synthetic data sets are carried out with different methods for comparison. The results show that our method has greater efficiency and lower distortion of the processed trajectories.

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Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

Community structure is one of the most important topological properties of complex networks, which can help us to understand the functions and guide the development of networks. In this article, a community detection algorithm is proposed based on local similarity and hierarchical clustering. Local similarity is used to measure link similarities instead of node similarities in order to form a similarity metric. Hierarchical clustering is used to gather all the links to form a hierarchical tree, and then cut the tree with the optimization value of modularity to get the community structure. Experiments on real-world and generated benchmark networks show the significant performance of the algorithm both in accuracy and efficiency.