Noise is required in differential privacy protection for spatial data with tree index. Most of the existing differential privacy budget methods adopt uniform allocation, and ordinary users can not personalize their choice. To solve this problem, an arithmetic sequence privacy budget allocation method and a geometric sequence privacy budget allocation method were proposed. Firstly, the spatial data was indexed by tree structure. Secondly, users could personalize the difference or ratio of privacy budgets assigned by two adjacent layers to dynamically adjust the privacy budget according to the needs of privacy protection and query accuracy. Finally, the privacy budget was allocated to each layer of tree to realize personalized and on-demand allocation. Theoretical analysis and experimental results show that these two methods are more flexible in the allocation of privacy budget than the uniform allocation method, and the geometric sequence allocation method is better than the arithmetic sequence allocation method.

Aiming at the problem of poor data anonymity and large data loss caused by excessive suppression of traditional high-dimensional trajectory privacy protection model, a new trajectory-privacy method based on information entropy suppression was proposed. A flowgraph based on entropy was generated for the trajectory dataset, a reasonable cost function according to the information entropy of spatio-temproal points was designed, and the privacy was preserved by local suppression of spatio-temproal points. Meanwhile, an improved algorithm for comparing the similarity of flowgraphs before and after suppression was proposed, and a function for evaluating the privacy gains was introduced.Finally, the proposed method was compared with the LK-Local (Length K-anonymity based on Local suppression) approach in trajectory privacy and data practicability. The experimental results on a synthetic subway transportation system dataset show that, with the same anonymous parameter value the proposed method increases the similarity measure by about 27%, reduces the data loss by about 25%, and increases the privacy gain by about 21%.

Focusing on the issue of privacy disclosure between trajectory and non-sensitive information, a trajectory privacy preserving algorithm based on non-sensitive information analysis was proposed. Firstly, the correlation between trajectory and non-sensitive information was analyzed to build trajectory privacy disclosure decision model, and the Minimal Violating Sequence tuple (MVS) was gotten. Secondly, using common subsequences, the doublets with the minimal loss of trajectory data in MVS were selected as the suppression objects when removing the privacy risks caused by MVS, then the anonymized trajectory dataset with privacy and low data loss was obtained. In the comparison experiments with LKC-Local algorithm and Trad-Local algorithm, when the sequence length is 3, the average instance loss of the proposed algorithm is decreased by about 6% and 30% respectively, and the average MFS (Maximal Frequent Sequence) loss is decreased by about 7% and 60% respectively. The experimental results verify that the proposed algorithm can effectively improve the quality of recommend service.

Focusing on the issue that the existing co-location pattern mining algorithms fail to effectively address the problem of unevenly distributed spatial objects, a hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects was proposed. Firstly, an unevenly distributed dataset generation method was put forward. Secondly, the unevenly distributed dataset was partitioned by a hierarchical mining method in order to provide each region with an even spatial distribution. Finally, the spatial data mining of the separated fuzzy objects was conducted by means of the improved PO_RI_PC algorithm. Based on the distance variation coefficient, the neighborhood relationship graph for each sub-region was constructed to complete the regional fusion, and then the co-location pattern mining was realized. The experimental results show that, compared to the traditional method, the proposed method has higher execution efficiency. With the change of the number of instances and uneven degree, more co-location sets are mined, and the average increase reaches about 25% under the same condition, more accurate mining results are obtained through this method.