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.

Density peaks clustering is an efficient density-based clustering algorithm. However, it is sensitive to the global parameter d_c. Furthermore, artificial intervention is needed for decision graph to select clustering centers. To solve these problems, a new density peaks clustering algorithm based on shared near neighbors similarity was proposed. Firstly, the Euclidean distance and shared near neighbors similarity were combined to define the local density of a sample, which avoided the setting of parameter d_c of the original density peaks clustering algorithm. Secondly, the selection process of clustering centers was optimized to select initial clustering centers adaptively. Finally, each sample was assigned to the cluster as its nearest neighbor with higher density samples. The experimental results show that, compared with the original density peaks clustering algorithm on the UCI datasets and the artificial datasets, the average values of accuracy, Normalized Mutual Information (NMI) and F-Measure of the proposed algorithm are respectively increased by about 22.3%, 35.7% and 16.6%. The proposed algorithm can effectively improve the accuracy of clustering and the quality of clustering results.

Focusing on the attack of frequent location as background knowledge causing user identity disclosure in geosocial network, a privacy-preserving algorithm based on frequent location was proposed. Firstly, The frequent location set was generated by the frequency of user check-in which was allocated for every user. Secondly,according to the background knowledge, hyperedges were composed by frequent location subset. Some hyperedges were remerged which did not meet anonymity parameter k, meanwhile the minimum bias of user and bias of location were chosen as hyperedges remerging metrics. Finally, in the comparison experiments with ( k,m)-anonymity algorithm, when the background knowledge was 3, the average bias of user and bias of location were decreased by about 19.1% and 8.3% on dataset Gowalla respectively, and about 22.2% and 10.7% on dataset Brightkite respectively. Therefore, the proposed algorithm can effectively preserve frequent location privacy, and reduces bias of user and location.

Aiming at the vulnerability to continuous query attacks in the methods based on k-anonymity and difficultly in constructing anonymous region when the number of users is few, a method for trajectory privacy protection based on district partitioning was proposed. A user-group that has the history query points of a particular district was obtained by using a third-party auxiliary server, and the historical query points were downloaded from the users in the user-group by P2P protocol. Then the query result was searched in the historical query information to improve the query efficiency. In addition, a pseudo query point was sent to confuse attackers, and the multiple query points were hidden in the same sub-district by district partitioning to keep the attackers from reconstructing real trajectory of the user to ensure security. The experimental results show that the proposed method can improve the security of user trajectory privacy with the increases of distance and cache time. Compared to the Collaborative Trajectory Privacy Preserving (CTPP) method, when the number of users is 1500, the security is averagely increased about 50% and the query efficiency is averagely improved about 35% (the number of sub-districts is 400).

Aiming at the problem of privacy leakage in the application of traditional clustering algorithm, a spectral clustering algorithm based on differential privacy protection was proposed. Based on the differential privacy model, the cumulative distribution function was used to generate random noise that satisfies Laplasse distribution. Then the noise was added to the sample similarity function calculated by the spectral clustering algorithm, which disturbed the weight values between the individual samples and realized information hiding between sample individuals for privacy protection. Experimental results of UCI dataset verify that the proposed algorithm can achieve effective data clustering within a certain degree of information loss, and can also protect clustered data.

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.