Privacy preserving semantic trajectory data publishing for mobile location-based services
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Privacy preserving semantic trajectory data publishing for mobile location-based services Rong Tan1 • Yuan Tao2 • Wen Si1 • Yuan-Yuan Zhang3
The Author(s) 2019
Abstract The development of wireless technologies and the popularity of mobile devices is responsible for generating large amounts of trajectory data for moving objects. Trajectory datasets have spatiotemporal features and are a rich information source. The mining of trajectory data can reveal interesting patterns of human activities and behaviors. However, trajectory data can also be exploited to disclose users’ privacy information, e.g., the places they live and work, which could be abused by a malicious user. Therefore, it is very important to protect the users’ privacy before publishing any trajectory data. While most previous research on this subject has only considered the privacy protection of stay points, this paper distinguishes itself by modeling and processing semantic trajectories, which not only contain spatiotemporal data but also involve POI information and the users’ motion modes such as walking, running, driving, etc. Accordingly, in this research, semantic trajectory anonymizing based on the k-anonymity model is proposed that can form sensitive areas that contain k - 1 POI points that are similar to the sensitive points. Then, trajectory ambiguity is executed based on the motion modes, road network topologies and road weights in the sensitive area. Finally, a similarity comparison is performed to obtain the recordable and releasable anonymity trajectory sets. Experimental results show that this method performs efficiently and provides high privacy levels. Keywords Mobile services Location-based services Semantic trajectory Trajectory privacy protection
1 Introduction Due to the development of mobile devices and positioning technologies, various kinds of mobile positioning devices, such as car navigation systems, GPS-enabled mobile & Yuan-Yuan Zhang [email protected] Rong Tan [email protected] Yuan Tao [email protected] Wen Si [email protected] 1
College of Information and Computer Science, Shanghai Business School, Shanghai 201400, China
2
Computing Center, Shanghai University, Shanghai 200444, China
3
College of Information Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China
phones, mobile wearable devices, tablet computers and position sensors, have been made available to consumers in recent years [1–5]. The popularity of mobile positioning devices has spawned numerous location-based services (LBSs) [6–8] and has generated large amounts of locational data as well [9–11]. According to statistics, each moving object in LBSs transmits its current location every 15 s on average, which indicates that more than 100 million pieces of location information are transmitted per second. And the data are extensively applied in everyday life, thereby constantly influencing people’s lifestyles, working habits and thinking modes. By making observations of a pers
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