Private Trajectory Data Publication for Trajectory Classification

Huaijie Zhu, Xiaochun Yang, Bin Wang, Leixia Wang, Wang Chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationWeb Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings
EditorsWeiwei Ni, Xin Wang, Wei Song, Yukun Li
PublisherSpringer
Pages347-360
Number of pages14
ISBN (Print)9783030309510
DOIs
StatePublished - Jan 1 2019
Event16th Web Information Systems and Applications Conference, WISA 2019 - Qingdao, China
Duration: Sep 20 2019Sep 22 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11817 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Web Information Systems and Applications Conference, WISA 2019
CountryChina
CityQingdao
Period9/20/199/22/19

Fingerprint

Trajectories
Trajectory
Privacy
Classify
Real-world Applications
Moving Objects
Support vector machines
Labels
Support Vector Machine
Classifiers
Classifier

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhu, H., Yang, X., Wang, B., Wang, L., & Lee, W. C. (2019). Private Trajectory Data Publication for Trajectory Classification. In W. Ni, X. Wang, W. Song, & Y. Li (Eds.), Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings (pp. 347-360). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11817 LNCS). Springer. https://doi.org/10.1007/978-3-030-30952-7_35
Zhu, Huaijie ; Yang, Xiaochun ; Wang, Bin ; Wang, Leixia ; Lee, Wang Chien. / Private Trajectory Data Publication for Trajectory Classification. Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings. editor / Weiwei Ni ; Xin Wang ; Wei Song ; Yukun Li. Springer, 2019. pp. 347-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Private Trajectory Data Publication for Trajectory Classification",
abstract = "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.",
author = "Huaijie Zhu and Xiaochun Yang and Bin Wang and Leixia Wang and Lee, {Wang Chien}",
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Zhu, H, Yang, X, Wang, B, Wang, L & Lee, WC 2019, Private Trajectory Data Publication for Trajectory Classification. in W Ni, X Wang, W Song & Y Li (eds), Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11817 LNCS, Springer, pp. 347-360, 16th Web Information Systems and Applications Conference, WISA 2019, Qingdao, China, 9/20/19. https://doi.org/10.1007/978-3-030-30952-7_35

Private Trajectory Data Publication for Trajectory Classification. / Zhu, Huaijie; Yang, Xiaochun; Wang, Bin; Wang, Leixia; Lee, Wang Chien.

Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings. ed. / Weiwei Ni; Xin Wang; Wei Song; Yukun Li. Springer, 2019. p. 347-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11817 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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

PY - 2019/1/1

Y1 - 2019/1/1

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.

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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

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A2 - Li, Yukun

PB - Springer

ER -

Zhu H, Yang X, Wang B, Wang L, Lee WC. Private Trajectory Data Publication for Trajectory Classification. In Ni W, Wang X, Song W, Li Y, editors, Web Information Systems and Applications - 16th International Conference, WISA 2019, Proceedings. Springer. 2019. p. 347-360. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30952-7_35