To solve the problem of k nearest neighbor query in obstacle space, a k nearest neighbor Query method based on improved Parallel Ant colony algorithm (PAQ) was proposed. Firstly, ant colonies with different kinds of pheromones were utilized to search k nearest neighbors in parallel. Secondly, a time factor was added as a condition of judging path length to directly show the searching time of ants. Thirdly, the concentration of initial pheromone was redefined to avoid the blind searching of ants. Finally, visible points were introduced to divide the obstacle path into multiple Euclidean paths, meawhile the heuristic function was improved and the visible points were selected by ants to conduct probability transfer making ants search in more proper direction and prevent the algorithm from falling into local optimum early. Compared to WithGrids method, with number of data points less than 300, the running time for line segment obstacle is averagely reduced by about 91.5%, and the running time for polygonal obstacle is averagely reduced by about 78.5%. The experimental results show that the running time of the proposed method has obvious advantage on small-scale data, and the method can process polygonal obstacles.

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.

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).

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.

Concerning the problem that it is difficult for the users in cloud computing to obtain the high-quality and personalized cloud services provided by a large number of cloud providers, a trust model based on user types and privacy protection for the personalized cloud services was proposed. Firstly, the users were divided into familiar users, strange users and normal users according to the transaction history. Secondly, a fair and reasonable trust evaluation Agent was introduced to protect users' privacy, which could evaluate the trust relationship between requesters and providers based on the user types. Lastly, in view of the dynamics of trust, a new updating mechanism combined with the transaction time and transaction amount was provided based on Quality of Service (QoS). The simulation results show that the proposed model has higher transaction success rate than AARep and PeerTrust. The transaction success rate can be increased by 10% and 16% in the harsh environment where the malicious user ratio reaches 70%. This method can improve transaction success rate, and has a strong ability to withstand harsh environments.

In order to protect user's identity privacy and data security for access control in wireless sensor networks, a privacy-preserving access control protocol in multi-user wireless sensor networks was proposed. The protocol employed attribute-based encryption algorithms and distributed access control mode, using the attribute certificates, digital signatures and threshold mechanism to achieve the pay access, fine-grained access control and anonymous access. And it also ensured the confidentiality of data transmission and data integrity of query command. Analysis and protocol comparison shows that the proposed protocol has several advantages over the current access control methods: lower cost in computation, communication and storage, better scalability and better adaptation to access control requirement of payment wireless sensor network.