Driver Locations Harvesting Attack on pRide
Shyam Murthy, Srinivas Vivek

TL;DR
This paper reveals a passive attack on the pRide privacy-preserving ride-hailing protocol, demonstrating that an honest-but-curious rider can recover driver locations with high accuracy, compromising privacy despite blinding measures.
Contribution
It introduces a novel passive inference attack on pRide, showing how blinded distances can be exploited to reveal driver locations, highlighting privacy vulnerabilities in the protocol.
Findings
Can recover at least 80% of driver locations in experiments
Attack effective with a single ride request and blinded distances
Highlights privacy risks in homomorphic distance computations
Abstract
Privacy preservation in Ride-Hailing Services (RHS) is intended to protect privacy of drivers and riders. pRide, published in IEEE Trans. Vehicular Technology 2021, is a prediction based privacy-preserving RHS protocol to match riders with an optimum driver. In the protocol, the Service Provider (SP) homomorphically computes Euclidean distances between encrypted locations of drivers and rider. Rider selects an optimum driver using decrypted distances augmented by a new-ride-emergence prediction. To improve the effectiveness of driver selection, the paper proposes an enhanced version where each driver gives encrypted distances to each corner of her grid. To thwart a rider from using these distances to launch an inference attack, the SP blinds these distances before sharing them with the rider. In this work, we propose a passive attack where an honest-but-curious adversary rider who makes…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Vehicular Ad Hoc Networks (VANETs) · Privacy, Security, and Data Protection
