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Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services

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Security and Trust Management (STM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13075))

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Abstract

Ride-Hailing Service (RHS) has motivated the rise of innovative transportation services. It enables riders to hail a cab or private vehicle at the roadside by sending a ride request to the Ride-Hailing Service Provider (RHSP). Such a request collects rider’s real-time locations, which incur serious privacy concerns for riders. While there are many location privacy-preserving mechanisms in the literature, few of them consider mobility patterns or location semantics in RHS. In this work, we propose a pick-up location recommendation scheme with location indistinguishability and semantic indistinguishability for RHS. Specifically, we give formal definitions of location indistinguishability and semantic indistinguishability. We model the rider mobility as a time-dependent first-order Markov chain and generates a rider’s mobility profile. Next, it calculates the geographic similarity between riders by using the Mallows distance and classifies them into different geographic groups. To comprehend the semantics of a location, it extracts such information through user-generated content from two popular social networks and obtains the semantic representations of locations. Cosine similarity and unified hypergraph are used to compute the semantic similarities between locations. Finally, it outputs a set of recommended pick-up locations. To evaluate the performance, we build our mobility model over the real-world dataset GeoLife, analyze the computational costs of a rider, show the utility, and implement it on an Android smartphone. The experimental results show that it costs less than 0.12 ms to recommend 10 pick-up locations within 500 m of walking distance.

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Acknowledgment

The work described in this paper was supported by National Natural Science Foundation of China (NSFC) under the grant No. 62002094 and Anhui Provincial Natural Science Foundation under the grant No. 2008085MF196. It is partially supported by EU LOCARD Project under Grant H2020-SU-SEC-2018-832735. This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme granted to Dr. Meng Li.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei, China

    Yifei Chen, Meng Li & Shuli Zheng

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

    Yifei Chen, Meng Li & Shuli Zheng

  3. Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei, China

    Yifei Chen, Meng Li & Shuli Zheng

  4. Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, China

    Yifei Chen, Meng Li & Shuli Zheng

  5. Delft University of Technology, Delft, Netherlands

    Chhagan Lal

  6. Department of Mathematics, University of Padua, 35131, Padua, Italy

    Mauro Conti

Authors
  1. Yifei Chen
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  2. Meng Li
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  3. Shuli Zheng
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  4. Chhagan Lal
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  5. Mauro Conti
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Corresponding author

Correspondence to Meng Li .

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Editors and Affiliations

  1. University of Málaga, Málaga, Spain

    Rodrigo Roman

  2. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

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Chen, Y., Li, M., Zheng, S., Lal, C., Conti, M. (2021). Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services. In: Roman, R., Zhou, J. (eds) Security and Trust Management. STM 2021. Lecture Notes in Computer Science(), vol 13075. Springer, Cham. https://doi.org/10.1007/978-3-030-91859-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-91859-0_3

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  • Print ISBN: 978-3-030-91858-3

  • Online ISBN: 978-3-030-91859-0

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