pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

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.

TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time

Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

SmartLOC

On-demand delivery is a rapidly developing business worldwide, where meals and groceries are delivered door to door from merchants to customers by the couriers. Couriers' real-time localization plays a key role in on-demand delivery for all parties like the platform's order dispatching, merchants' order preparing, couriers' navigation, and customers' shopping experience. Although GPS has well solved outdoor localization, indoor localization is still challenging due to the lack of large-coverage, low-cost anchors. Given the high penetration of smartphones in merchants and frequent rendezvous between merchants and couriers, we employ merchants' smartphones as indoor anchors for a new sensing opportunity. In this paper, we design, implement and evaluate SmartLOC, a map-free localization system that employs merchants' smartphones as anchors to obtain couriers' real-time locations. Specifically, we design a rendezvous detection module based on Bluetooth Low Energy (BLE), build indoor shop graphs for each mall, and adopt graph embedding to extract indoor shops' topology. To guarantee anchors' accuracy and privacy, we build a mutual localization module to iteratively infer merchants' state (in-shop or not) and couriers' locations with transformer models. We implement SmartLOC in a large on-demand delivery platform and deploy the system in 566 malls in Shanghai, China. We evaluate SmartLOC in two multi-floor malls in Shanghai and show that it can improve the accuracy of couriers' travel time estimation by 24%, 43%, 70%, and 76% compared with a straightforward graph solution, GPS, Wi-Fi, and TransLoc.