A Trajectory Privacy Protect Method Based on Location Pair Reorganization. (5th July 2022)

• Record Type: Journal Article
• Authors: Wu, Wanqing; Shang, Wenlong; Lei, Ruohe; Yang, Xin
• Other Names: Rajput Dharmendra Singh Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-05
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/8635275
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the rapid development of mobile Internet and communication technology, location-based services (LBS) are widely used in our daily life. The server stores a large amount of user location data, and these location data constitute user trajectories. If trajectory information on the server is leaked, it will seriously endanger users' privacy. Trajectory k -anonymity technology is one of the most important methods to protect the privacy of user trajectory. However, current trajectory k -anonymity methods have less discussion on the semantic of stop point when selecting dummy trajectory, which leads to the fact that attacker can still exclude the dummy trajectory from the k -anonymity set and infer the real trajectory by combining background knowledge with the semantic information of stop points. To address this problem, this paper decomposes the real trajectory into location pairs set; the set consists of start-end points and stop points. According to the similarity of location pairs, the similar location pairs in history trajectory set are used to generate dummy trajectory: firstly, extracting the start-end points and stop points from real trajectory and assigning semantic to them. Then, based on the semantic, temporal, and geographical attributes, eligible location pairs are selected from history trajectory set to construct equivalence class. Finally, according to the location pairs in equivalence class, k − 1 dummy trajectories are generated to form a k -anonymity set. We evaluate our method thoroughly with real dataset. The results show that our method achieve an effective data availability and higher privacy protection than other methods.