The high similarity between trajectories in anonymity set may lead to the trajectory privacy leak. In order to solve the problem, a trajectory privacy preserving algorithm based on trajectory shape diversity was proposed. The exiting pre-processing method was improved to reduce the loss of information through trajectory synchronization processing. And by l-diversity, the trajectories with shape diversity were chosen as the members of the anonymity set when greedy clustering. Too high shape similarity between member trajectories of the set was prevented to avoid the attack of trajectory shape similarity. The theoretical analysis and experimental results show that, the proposed algorithm can realize k-anonymity of trajectory and l-diversity concurrently, reduce the running time and trajectory information loss, increase the trajectory data availability and realize better privacy protection. The proposed algorithm can be effectively applied to the privacy-preserving trajectory data publishing.

The sensitive relationships between users are important privacy information in social networks. Focusing on the issue of sensitive relationships leakage between users, an edge partitioning algorithm was proposed. Firstly, every non-sensitive edge was partitioned into some sub-edges after the sensitive edge was deleted in social networks. Secondly, every sub-edge was assigned information which belongs to the original non-sensitive edge. So every sub-edge contained part information of the original non-sensitive edge. The anonymized social network that preserves privacy was generated finally. In the comparison experiments with cluster-edge algorithm and cluster-based with constraints algorithm, the edge partitioning algorithm had a greater decrease of the probability of sensitive relationships leakage with maintaining high availability of data. The probability was decreased by about 30% and 20% respectively. As a result, the edge partitioning algorithm can effectively protect sensitive relationships in social networks.