TY - GEN
T1 - Private Trajectory Data Publication for Trajectory Classification
AU - Zhu, Huaijie
AU - Yang, Xiaochun
AU - Wang, Bin
AU - Wang, Leixia
AU - Lee, Wang Chien
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Trajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, which can be released to the public or third parties. In this paper, we study private trajectory publication for trajectory classification (PTPTC), which not only preserves the trajectory privacy, but also guarantees high TC accuracy. We propose a private trajectory data publishing framework for TC, which constructs an anonymous trajectory set for publication and use in data services to classify the anonymous trajectories. In order to build a “good” anonymous trajectory set (i.e., to guarantee a high TC accuracy), we propose two algorithms for constructing anonymous trajectory set, namely Anonymize-POI and Anonymize-FSP. Next, we employ Support Vector Machine (SVM) classifier to classify the anonymous trajectories. Finally, the experimental results show that our proposed algorithms not only preserve the trajectory privacy, but also guarantee a high TC accuracy.
AB - Trajectory classification (TC), i.e., predicting the class labels of moving objects based on their trajectories and other features, has many important real-world applications. Private trajectory data publication is to anonymize trajectory data, which can be released to the public or third parties. In this paper, we study private trajectory publication for trajectory classification (PTPTC), which not only preserves the trajectory privacy, but also guarantees high TC accuracy. We propose a private trajectory data publishing framework for TC, which constructs an anonymous trajectory set for publication and use in data services to classify the anonymous trajectories. In order to build a “good” anonymous trajectory set (i.e., to guarantee a high TC accuracy), we propose two algorithms for constructing anonymous trajectory set, namely Anonymize-POI and Anonymize-FSP. Next, we employ Support Vector Machine (SVM) classifier to classify the anonymous trajectories. Finally, the experimental results show that our proposed algorithms not only preserve the trajectory privacy, but also guarantee a high TC accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85075609588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075609588&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30952-7_35
DO - 10.1007/978-3-030-30952-7_35
M3 - Conference contribution
AN - SCOPUS:85075609588
SN - 9783030309510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 347
EP - 360
BT - Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings
A2 - Ni, Weiwei
A2 - Wang, Xin
A2 - Song, Wei
A2 - Li, Yukun
PB - Springer
T2 - 16th Web Information Systems and Applications Conference, WISA 2019
Y2 - 20 September 2019 through 22 September 2019
ER -