pRide: Privacy-Preserving Online Ride Hailing Matching System With Prediction

Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users' privacy, much effort has b...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-08, Vol.70 (8), p.7413-7425
Main Authors: Huang, Junxin, Luo, Yuchuan, Fu, Shaojing, Xu, Ming, Hu, Bowen
Format: Article
Language:English
Subjects:
Algorithms
Car sharing
Cost analysis
Data privacy
Dispatching
Electronic devices
empty distance
Machine learning
Matching
matching system
Online ride hailing
Optimization
Predictive models
Privacy
privacy-preserving
request prediction
Servers
Transportation services
Vehicles
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Summary:Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users' privacy, much effort has been done to construct a privacy-enhanced ORH system in recent years. However, almost all of current privacy-preserving schemes match the nearest driver to the requesting rider, it is not the optimal matching strategy and unnecessary waste of travel distance may be caused in this way. To solve the problem, we propose a privacy-preserving ORH scheme named pRide , which minimizes the overall travel distance by matching the best driver in global perspective instead of the nearest driver in the local region. To find the best driver, we utilize a deep learning model to predict emergence of ride requests in various regions, and enables the ORH server to perform ride matching leveraging prediction results. Based on the framework, we also propose optimizations to enhance pRide . Moreover, to further defend inference attacks from riders, we propose a private comparing algorithm at the cost of a little communication overhead. Through theoretical analysis and experiments, we prove that pRide is able to decrease the overall travel distance securely and efficiently in ORH matching scenarios.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3090042