Historical Trajectories Based Location Privacy Protection Query

With the wide adoption of intelligent mobile phone with global positioning functionalities, location based services (LBS) are more and more popular. Location privacy becomes an important issue. The current methods on privacy protection can be classified into the following folds: privacy preserving LBS query in a real time manner, privacy protection on historical trajectory data publication afterwards and location based recommendation. In this paper, we investigate the location related privacy problem in a different way by utilizing the increasing volume of historical trajectory data for personal privacy analysis, which can help LBS users have a better understanding on their privacy status. We propose an efficient way to organize the spatial and temporal data, which integrates quad-tree and hash function based on time slots as index. By analyzing user privacy requirements on LBS, we propose two basic queries and algorithms, which verify an LBS user´s privacy status at certain location and time. Considering complex privacy query, we propose some combined algorithms to solve the continuous privacy query problem on both spatial and temporal dimensions. Experiments are performed on real dataset to verify our method.