To preserve privacy in trajectory data, most existing approaches adapt cloaking techniques to protect individual location points or clustering and perturbation techniques to protect entire trajectories. To confirm to the k-anonymity model, they first group locations/trajectories and then modify location points to ensure a cluster of k location points/trajectories are close to each other. However, when k is large or the time span of trajectories is long, the cluster based k-anonymity approaches will suffer from great distortion and lead to misleading analysis results. Observing that it is unnecessary to brutally provide the same level of privacy protection to all locations, we analyze the visiting status of a semantic place at which a point is situated as well as the distribution of neighboring semantic places and infer four privacy risk levels gbased on the risk of privacy breach. Then, we propose the Semantic Space Translation (SST) algorithm that adapts different strategies accordingly to modify locations so that it can strike a good balance between privacy preserving and data utility. To verify the performance of our approach, we conduct several experiments and the experimental results show that our idea is feasible and the SST is effective.