Abstract

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Highlights

  • Received: 24 October 2021With the advent of GPS-embedded mobile devices and digital maps, Online RideHailing (ORH) services have gained much popularity and have seen remarkable development in the past decade

  • Privacy-preserving online ride-sharing matching scheme pShare, which performs matching between a shared driver and group riders with minimum detouring distance, while protecting their location privacy against the ORH service provider

  • Accuracy: In the experiments, we use two metrics to evaluate the accuracy of the system, which are Successful Matching Rate (SMR) and Accurate Matching Rate (AMR), respectively

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Summary

Introduction

With the advent of GPS-embedded mobile devices and digital maps, Online RideHailing (ORH) services have gained much popularity and have seen remarkable development in the past decade. ORH companies such as Uber and Didi provide a platform for riders to request a ride using few operations through their mobile devices, which has had a revolutionary impact on the ride-hailing industry [1]. Ride-matching is the primary function of ORH service, and it usually matches the nearest driver for any rider who starts a ride-matching request. To request the ride-matching service, the rider needs to submit his location information to the ORH service provider (i.e., the ORH company) through the ride-hailing application on their smartphone. The service provider matches the nearby drivers who update their locations in real time

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