Due to the popularity of smart mobile terminals, sensitive information such as personal location privacy, check-in data privacy and trajectory privacy in the collected spatio-temporal data are easy to be leaked. In the current researches, protection technologies are proposed for the above privacy leakages respectively, and there is not a personalized spatio-temporal data privacy protection method to prevent the above privacy leakages for users. Therefore, a personalized privacy protection model for spatio-temporal data named ( p, q, ε)-anonymity and a Personalized Privacy Protection for Spatio-Temporal Data (PPP _ST) algorithm based on this model were proposed to protect the users' privacy data with personalized settings (location privacy, check-in data privacy and trajectory privacy). The heuristic rules were designed to generalize the spatio-temporal data to ensure the availability of the published data and realize the high availability of spatio-temporal data. In the comparison experiments, the data availability rate of PPP _ST algorithm is about 4.66% and 15.45% higher than those of Information Data Used through K-anonymity (IDU-K) and Personalized Clique Cloak (PCC) algorithms on average respectively. At the same time, the generalized location search technology was designed to improve the execution efficiency of the algorithm. Experiments and analysis were conducted based on real spatio-temporal data. Experimental results show that PPP _ST algorithm can effectively protect the privacy of personalized spatio-temporal data.

The existed TD-FTT (Time Dependent Fast Travel Time) algorithm, for answering K Nearest Neighbors ( KNN) query in time dependent road network, requires that the issued time and the arrival time of a query must be in the same time interval, which costs a long time in the preprocessing phase. To solve these problems, an Improved TD-FTT (ITD-FTT) algorithm based on dynamically selecting heuristic values was proposed. Firstly, in the preprocessing phase, the road network G _min with the minimum cost for each time interval was created by using time functions of edges. Secondly, a parallel method of utilizing Network Voronoi Diagram (NVD) in road network G _min was used to compute the nearest neighbors of nodes to reduce the time cost. Finally, in the query phase, the heuristic value was dynamically selected to get rid of the time interval limitation by calculating the time interval of the current arrival time of nodes. The experimental results show that in the preprocessing phase, the time cost of ITD-FTT is reduced by 70.12% compared with TD-FTT. In the query phase, the number of traversal nodes of ITD-FTT is 46.52% and 16.63% lower than TD-INE (Time Dependent Incremental Network Expansion) and TD-A (Time Dependent A star) algorithm respectively, and the response time of ITD-FTT is 47.76% and 18.24% lower than TD-INE and TD-A. The experimental results indicate that the ITD-FTT algorithm reduces the number of nodes by query expansion, decreases the time of searching the KNN results and improves the query efficiency.