Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services
Chen, Yifei (Hefei University of Technology)
Li, Meng (Hefei University of Technology)
Zheng, Shuli (Hefei University of Technology)
Lal, C. (TU Delft Cyber Security)
Conti, M. (TU Delft Cyber Security)
Roman, Rodrigo (editor)
Zhou, Jianying (editor)
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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Springer, Darmstadt, Germany
Security and Trust Management - 17th International Workshop, STM 2021, Proceedings, 13075 (17)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13075 LNCS
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© 2021 Yifei Chen, Meng Li, Shuli Zheng, C. Lal, M. Conti