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A Survey of Privacy-Preserving Techniques on Trajectory Data

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Parallel Architectures, Algorithms and Programming (PAAP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1163))

Abstract

How to protect user’s trajectory privacy while ensuing the user’s access to high quality services is the core of the study of trajectory privacy protection technology. With the rapid development of mobile devices and Location Based Service (LBS), the amount of locations and trajectories of moving objects collected by service providers is continuously increasing. On one hand, the collected trajectories contains rich spatial-temporal information, and its analysis and mining can support a variety of innovative applications. Since trajectories enable intrusive inferences which may expose private information, such as individual habits, behavioral patterns, social relationships and so on, directly publishing trajectories may result in individual privacy vulnerable to various threats. On the other hand, the existing techniques are unable to prevent trajectory privacy leakage, so the complete real-time trajectories of individuals may be exposed when they request for LBS, even if their location privacy is protected by common data protection mechanisms. Therefore, specific techniques for trajectory privacy preserving have been proposed in accordance with different application requirements. In the trajectory data publishing scenario, privacy preserving techniques must preserve data utility. In the LBS scenario, privacy preserving techniques must guarantee high quality of services. In this survey, we overview the key challenges and main techniques of trajectory privacy protection for the above requirements respectively.

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Acknowledgment

This work is supported by National Key R & D Program of China Project #2017YFB0203201, Science and Technology Program of Guangdong Province, China (No. 2017A010101039).

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Authors and Affiliations

  1. School of Data and Computer, Sun Yat-sen University, Guangzhou, Guangdong, China

    Songyuan Li, Hong Shen & Yingpeng Sang

  2. School of Computer Science, University of Adelaide, Adelaide, Australia

    Hong Shen

Authors
  1. Songyuan Li
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  2. Hong Shen
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  3. Yingpeng Sang
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Correspondence to Songyuan Li .

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Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, China

    Hong Shen

  2. Sun Yat-sen University, Guangzhou, China

    Yingpeng Sang

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Li, S., Shen, H., Sang, Y. (2020). A Survey of Privacy-Preserving Techniques on Trajectory Data. In: Shen, H., Sang, Y. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-2767-8_41

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  • DOI: https://doi.org/10.1007/978-981-15-2767-8_41

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