Driver Locations Harvesting Attack on pRide Shyam Murthy000000020222322X Srinivas Vivek0000000284260859

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Driver Locations Harvesting Attack on pRide

Shyam Murthy[0000−0002−0222−322X]

Srinivas Vivek[0000−0002−8426−0859]

International Institute of Information Technology Bangalore, Bengaluru, India.

shyam.sm@iiitb.ac.in

srinivas.vivek@iiitb.ac.in

Abstract. Privacy preservation in Ride-Hailing Services (RHS) is in-

tended 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 Eu-

clidean 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 se-

lection, 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 Near-

est Road API, the adversary can obtain the precise locations of respond-

ing drivers. We conduct experiments with random on-road driver loca-

tions for four diﬀerent cities. Our experiments show that we can deter-

mine the precise locations of at least 80% of the drivers participating in

the enhanced pRide protocol.

Keywords: Ride-Hailing Services, Privacy and Censorship, Attacks

1 Introduction

According to a recent research by MordorIntelligence [8], the global Ride-Hailing

Services (RHS) market, valued at USD 113 billion in 2020, is expected to reach

This version of the contribution has been accepted for publication, after

peer review (when applicable) but is not the Version of Record and does

not reﬂect post-acceptance improvements, or any corrections. The Version of

Record is available online at: http://dx.doi.org/10.1007/978-3-031-23020-2_36.

Use of this Accepted Version is subject to the publisher’s Accepted Manuscript

manuscript-terms

Corresponding Author: Shyam Murthy, shyam.sm@iiitb.ac.in

arXiv:2210.13263v3 [cs.CR] 4 Jan 2023

2 S. Murthy and S. Vivek

USD 230 billion by 2026. With such a huge reach, individual privacy and secu-

rity issues are always of primary concern. Ride-Hailing Service Providers (SP)

like Uber, Lyft, Ola provide services in many parts of the world. Among other

features, the SP facilitates ride booking and fare payment options for their cus-

tomers, namely riders who subscribe with the SP for RHS. Drivers of vehicles

such as cars and motorcycles sign-up with the SP in order to oﬀer rides. At the

time of subscription, the SP collects private information of riders and drivers in

order to provide services eﬀectively as well as required by local governance laws.

In addition, the SP collects statistics of riders and drivers for every ride that

is oﬀered in its network. This naturally brings up the topic of individual data

privacy concerns from both riders as well as drivers over their data held by the

SP. Also, curious or malicious drivers or riders might be interested in learning

more about the other parties. There are a number of works and their analysis

in the literature that look at privacy-preserving RHS, we list some of them in

Section 5.

Huang et al. proposed pRide [4], a privacy-preserving online RHS protocol

that aims to provide the optimum driver in a global perspective thereby minimiz-

ing the unnecessary travel distance to pick the rider. The protocol makes use of

a deep learning model to predict emergence of new ride requests in a ride-hailing

region to enable the SP to make use of such prediction while matching optimum

drivers to ride requests. They show that by using such a prediction model in a

global perspective, the overall distance travelled by a matching driver is mini-

mized compared with matching a nearest driver in the local region. The protocol

proposes to use a Somewhat Homomorphic Encryption (SHE) scheme to encrypt

rider and driver locations. The advantage of using a homomorphic encryption

scheme is that it allows computations on ciphertexts so that the result of compu-

tation is available only after decryption. Fully Homomorphic Encryption (FHE)

schemes that support potentially any number of homomorphic operations have

high cost in terms of large ciphertexts and high computation latency. Hence,

many practical applications that know, a priori, the bound on the number of

homomorphic operations, prefer to use SHE schemes. In the pRide paper, the

authors use the FV cryptosystem [1] in the implementation of their scheme.

Even though applications make use of semantically secure cryptosystems, care-

ful analysis is required to make sure no unintended security holes are introduced

while adapting the cryptosystem to their applications.

The pRide protocol, described in more detail in Section 2, has two parts,

the basic protocol and an enhanced version. We discuss the basic protocol in

this paragraph. In the initialization phase, SP divides the area of its operation

into grids, the details of which are made available to all parties. SP keeps a

record of ride requests emanating from each grid over speciﬁc time epochs and

trains a prediction model using this information. It then uses this information to

predict the grid-based distribution of requests for the next period, denoted by

P R(g), namely the prediction result for grid id g. Drivers, registered with the

SP, submit their current grid id to the SP so that the SP can maintain the driver

distribution map. A rider who wishes to hail a ride, picks a (public key, secret

Driver Locations Harvesting Attack on pRide 3

key) pair, encrypts her coordinates and sends the ciphertext and public key to

SP along with the ride request. When SP receives the ride request, it performs a

search for a suitable driver in a preset order of grids around the rider’s grid and

obtains a list of candidate drivers using the driver distribution map. SP then

forwards the ride request to all candidate drivers. To oﬀer their ride, drivers

respond to SP by encrypting their location using the rider’s public key. SP then

homomorphically computes the square of the Euclidean distance between rider

and drivers’ encrypted locations and forwards the same to the rider along with

P R(g)where gis the driver’s grid id. Rider decrypts the distances and picks

the shortest distance D0. It then performs two checks over the list of sorted

distances. First, is Di−D0< D0−Ddiag?, where Diis the distance for ith driver

and Ddiag is the length of the diagonal of the grid, and second, does the model

predict no new ride request emerging in the driver’s grid within a short time

period?When both these conditions are satisﬁed, the rider informs SP about

the selected index iafter which the SP facilitates secure ride establishment with

the rider and selected driver.

In order to optimize their ride matching, the paper proposes enhanced pRide

built on top of the basic pRide protocol, but having a diﬀerent method to pick

the optimum driver. They show that they get better results when a driver also

provides her encrypted distance to the farthest corner of her grid. This way the

rider can use that distance, instead of Ddiag in the aforementioned check to

select the optimum driver. However, the authors notice that if such a distance is

decrypted by an adversarial rider, she can launch an inference attack to obtain

driver’s locations. In order to thwart such an attack, the paper proposes a novel

method where the driver provides SP with her encrypted distances to the four

corners of her grid. SP then picks random integers to homomorphically blind

the distances before sharing the same with the rider. Rider then decrypts the

blinded distances and applies a private comparison algorithm which determines

the result of the inequality Di−D0< D0−Dmaxdist, where Dmaxdist is the

distance between the driver and the farthest corner of her grid g. Finally, using

this inequality and P R(g), it outputs the optimum selected driver.

As described earlier, in the enhanced pRide protocol, the SP homomorphi-

cally blinds the encrypted distances with random integers before sharing them

with the rider. In this paper, we show that such a blinding scheme is insecure,

whence an adversary rider can recover the underlying distances and then deduce

the locations of at least 80% of the drivers responding to a single ride request of

the rider when using the enhanced pRide protocol.

1.1 Comparison with ORide [12] Protocol

The pRide paper shows that their enhanced scheme is more eﬀective with the

same level of security as that of the basic version with only a small compromise

in its eﬃciency. In addition, by way of experiments, they show their computation

Henceforth in the paper, we use the term distance to mean squared Euclidean dis-

tance.

4 S. Murthy and S. Vivek

cost is signiﬁcantly better compared to a state-of-the-art protocol named ORide

[12]. We note here that the method in the basic pRide protocol where the SP

employs the homomorphic property of SHE to compute the Euclidean distance

between driver and rider to share the encrypted distances with rider is identical

to what is described in the ORide paper. The part that is diﬀerent is that in the

ORide paper to pick the nearest driver, only drivers inside the rider’s grid are

chosen as candidate drivers, whereas in the pRide protocol, only drivers outside

the rider’s grid are candidate drivers so as to optimize in a global perspective.

In [5], Kumaraswamy et al. demonstrated a driver locations harvesting attack

by honest-but-curious riders on the ORide protocol, where they determine the

exact locations of 40% of drivers participating in the ORide protocol. In the same

paper, the authors also provide a mitigation solution wherein a driver gives her

perturbed location instead of her actual location. The aforementioned attack on

the ORide protocol and the mitigating solution are both applicable to the basic

pRide protocol.

In [10], Murthy et al. demonstrated a driver locations harvesting attack,

again by honest-but-curious adversary riders, using triangulation on the ORide

protocol, where they show that they can determine the exact locations of all

participating drivers in the ORide protocol. Further, they extend their method

onto the mitigation solution suggested by [5] and show that they can determine

locations of between 25% to 50% of the participating drivers.

As mentioned earlier, in the pRide protocol, the method where the rider

obtains encrypted driver distances is identical to that in the ORide protocol.

Due to this, any location harvesting attack on ORide, like in the cases of [5] and

[10], are also directly applicable to the basic pRide protocol.

1.2 Our Contribution

We present a passive driver location harvesting attack on the enhanced pRide

protocol. The honest-but-curious adversary rider issues a single ride request with

a search radius (SR) = 1, such that grids adjacent to the rider’s grid are searched

(as explained in the pRide paper, Section V-B-4, pp. 6). In our attack, the

adversary rider receives, per driver, a set of encrypted blinded distances between

the driver’s location and each corner of the driver’s grid. One would expect that

such a blinding process would make it hard for the rider to deduce anything

about the underlying distances.

Rider decrypts the ciphertexts received from SP to obtain blinded distances.

Next, by computing the Greatest Common Divisor (GCD) of the blinded dis-

tances and eliminating common factors, the rider recovers the blinding values

after which the distances are easily obtained. Rider now has the four distances

from driver to each corner of the driver’s grid. Using these distances, the rider

computes four equiprobable driver locations in each of the four grids adjacent to

the rider’s grid. This is due to the fact that the distances are in random order

and, so, there is no correlation between each corner of the grid and its distance to

the driver. Rider knows the distance between herself and each responding driver.

Now, using the distance between herself and a particular responding driver (say,

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DriverLocationsHarvestingAttackonpRideShyamMurthy[000000020222322X]SrinivasVivek[0000000284260859]InternationalInstituteofInformationTechnologyBangalore,Bengaluru,India.shyam.sm@iiitb.ac.insrinivas.vivek@iiitb.ac.inAbstract.PrivacypreservationinRide-HailingServices(RHS)isin-tendedtoprotectprivacyofd...

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Driver Locations Harvesting Attack on pRide Shyam Murthy000000020222322X Srinivas Vivek0000000284260859

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