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

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • 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