标题：Historical Trajectories Based Location Privacy Protection Query
作者：Cao, Liu ;Sun, Yuqing ;Xu, Haoran
作者机构：[Cao, Liu ;Sun, Yuqing ;Xu, Haoran ] School of Computer Science and Technology, Taishan College, Shandong University, Jinan, China
会议名称：11th IEEE International Conference on Ubiquitous Intelligence and Computing and 11th IEEE International Conference on Autonomic and Trusted Computing and 14th IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014
会议日期：9 December 2014 through 12 December 2014
来源：Proceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014
关键词：Historical trajectory; Location privacy; Privacy query
摘要：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. © 2014 IEEE.