  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 a single
ride request and receives the blinded distances from SP can recover the
constants used to blind the distances. Using the unblinded distances, rider to
driver distance and Google Nearest Road API, the adversary can obtain the
precise locations of responding drivers. We conduct experiments with random
on-road driver locations for four different cities. Our experiments show that
we can determine the precise locations of at least 80% of the drivers
participating in the enhanced pRide protocol.
