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Privacy-preserving spatial keyword location-to-trajectory matching

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Abstract

Location-to-trajectory matching plays an important role in trajectory data management and analysis. In this paper, we propose and study a novel problem of privacy-preserving spatial keyword location-to-trajectory matching (PPSKLT Matching). Given a set O of locations with keywords, a set T of activity trajectories, a spatial matching threshold \(\theta _{\text{sp}}\), and a keyword matching threshold \(\theta _{\text{kw}}\), the PPSKLT matching finds all location-trajectory pairs from O and T while preserving the users’ privacy. We believe that the PPSKLT matching may benefit many mobile applications such as trajectory activity analysis, event tracking, and so on. The PPSKLT matching is challenging due to three reasons: (1) how to define the spatial keyword similarity measure between locations and trajectories, (2) how to prune the search space effectively, and (3) how to preserve the users’ privacy during query processing. To overcome these challenges and address the problem efficiently, we develop a novel network expansion algorithm (NEA). We define a pair of upper and lower bounds on the spatio-textual similarity to prune the search space. We also define a privacy-preserving mechanism to protect users’ privacy. We conduct extensive experiments on spatio-textual data sets to verify the performance of the developed algorithms.

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Notes

  1. While many location points in the road network may have informative keywords such as “restaurant”, “shopping mall”, etc., it is also possible that some location points in V are simply road connectors without any meaningful keywords. In such cases, we may consider that the function \({\mathcal {K}}(\cdot )\) returns the empty set.

  2. http://www.iscas.ac.cn/

  3. https://www.cs.utah.edu/~lifeifei/SpatialDataset.htm

  4. https://developer.foursquare.com/

  5. Specifically, the for-loop at Line 2 of Baseline-LT (Algorithm 2) and the for-loop at Line 2 of Baseline-TL (Algorithm 3) are parallelized.

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Acknowledgements

This study is supported by the Program of New Century Excellent Talents in Fujian Province University.

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Fujian, China

    Ning Wang, Jianping Zeng & Wenxing Hong

  2. College of Information and Smart Electromechanical Engineering, Xiamen Huaxia University, Fujian, China

    Ning Wang

  3. Information Communication Technology and Smart Education of Fujian Engineering Research Center, Fujian, China

    Ning Wang

  4. School of Computer and Information Engineering, Xiamen University of Technology, Fujian, China

    Shunzhi Zhu

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  1. Ning Wang
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Correspondence to Shunzhi Zhu.

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Wang, N., Zeng, J., Hong, W. et al. Privacy-preserving spatial keyword location-to-trajectory matching. Distrib Parallel Databases 38, 667–686 (2020). https://doi.org/10.1007/s10619-020-07290-2

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