HACIT: A Privacy Preserving and Low Cost Solution for Dynamic

Navigation and Forensics in VANET

Kevin Decoster and David Billard

University of Applied Sciences Western Switzerland in Geneva - HES-SO, Geneva, Switzerland

Keywords:

VANET, D2D, Distributed Systems, Ad-Hoc network, Privacy, Blockchain, Forensics.

Abstract:

The current architecture for VANET related services relies on a Client-Server approach and leads to numerous

drawbacks, such as network congestion due to the bottleneck problem or, more importantly, data privacy

concerns. Indeed, because of the network topology, trafﬁc must go through nodes which limit the bandwidth

and thus bound the overall network capacity. Finally, user data is collected and stored in servers, used by third

party services. However, these providers are known to treat lightly user privacy by selling or using the data for

their own purposes (Beresford and Stajano, 2004). By use of a decentralized and distributed communication

protocol (i.e. D2D), one can overcome these problems by spreading the communication burden to all nodes in

the mesh. By means of cryptographic techniques, we can ensure that the shared data is secured and controlled

at the end-user side. This paper presents a study and proposes a proof of concept of a decentralized and

distributed information system by means of a dynamic navigation system for VANET, using a low-cost solution

such as Wiﬁ or LTE-direct new 3GPPP protocol. This system preserves user privacy and is augmented with

forensics capabilities.

1 INTRODUCTION

While many vendors offer IoT devices or propose cars

with embedded networked devices, the notions of data

privacy and security are often considered as a minor

point, when the list of the device features is enume-

rated. However, law enforcement services, specia-

lized services, and digital forensic experts witness a

dramatic surge in criminal data leaks, espionage, so-

cial engineering exploits and abuse of industrial con-

trol systems (SCADA). The last semi-annual report

from MELANI (Melani, 2006), the Swiss agency de-

aling with cybersecurity within the Federal Intelli-

gence Service (NDB / SRC), stresses the importance

of handling the security vulnerabilities. At the EU

level, member states have consistently tried to imple-

ment legal frameworks to protect the processing of

personal data and the free movement of such data

within EU (Directive, 1995) and in April two year

ago, a directive imposed a binding legal framework

(EU, 2016). Therefore, all this constitutes sufﬁcient

warning to alert the security community and to trig-

ger advanced research. The research presented in this

paper concerns the protection of user privacy but also

the necessity for law enforcement to gather evidence

(”digital forensics”).

The proof of concept presented in this paper fo-

cuses on the collaboration of intelligent cars for de-

termining the best driving route and avoiding trafﬁc

jams using only local information transmitted by the

other cars, without using a central Internet service. By

forbidding the use of centralized services like Google

Maps or Tomtom Go Mobile, the user private data is

kept at the user’s premises and privacy is strengthe-

ned. For that purpose, we designed a decentralized

and distributed communication scheme for Vehicular

Ad-Hoc Network (VANET) with forensics capabili-

ties. In order to (1) maintain data privacy at the user

side and (2) avoid to rely on costly and embedded har-

dware such as embedded radar or computers in cars,

we focus on a low-cost solution using mobile to mo-

bile device communications (D2D) on Android de-

vice, and validate our model on numerical simulation

aided with a VANET simulator such as GAMA (Grig-

nard et al., 2013) or Veins (OMNET++ and SUMO)

(Veins, 2011).

The modeled system can be decomposed into

three layers:

1. The communication layer, which ensures Conﬁ-

dentiality, Integrity, and Availability (CIA) using

symmetric and asymmetric cryptography (end to

end). Furthermore, we must address the problem

454

Decoster, K. and Billard, D.

HACIT: A Privacy Preserving and Low Cost Solution for Dynamic Navigation and Forensics in VANET.

DOI: 10.5220/0006778404540461

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 454-461

ISBN: 978-989-758-293-6

of access control (Identiﬁcation, Authentication,

and Authorization) in a strong dynamic environ-

ment and anonymity (e.g. by the use of temporal

Id’s);

2. The message dissemination layer: once the secure

communication channel is established, the vehi-

cles (nodes) must send information about the traf-

ﬁc, current attributes, etc., while addressing the

problem of network overload;

3. The application layer, for the processing of the

gathered data. The two applications foreseen in

our testbed are (1) the rerouting using an off-line

partially stored map (i.e. OpenstreetMap API)

and (2) the forensics capability using a blockchain

method.

Furthermore, conﬁdentiality between nodes is en-

sured by asymmetric and symmetric cryptography

while authentication and forgery-proof logging will

be ensured by blockchain technology.

The technology enabling communication is a fra-

mework called GRCBox (Tornell et al., 2015), in-

stalled on a Raspberry Pi device, which allows ad-

hoc networking on Android phones. This paper lea-

ves aside the study of the new proposed technology

ProSe (Prasad et al., 2014), an LTE based techno-

logy which directly compete with the Dedicated Short

Range Communication (DSRC) algorithm used for

VANET networking. It was proposed in Release 12 of

the 3GPP speciﬁcation for device to device commu-

nication using licensed spectrum. While the deploy-

ment is still in progress, we foresee that this techno-

logy can enhance our proposed algorithm. More ge-

nerally, the communication medium capability is a

key research issue in our work.

The rest of the paper is organized as follow.

Section 2 expresses the current state of the art regar-

ding the proposed functionalities. Section 3 presents

the chosen system models and answers the problem

of communication, message dissemination and dyna-

mic rerouting. Section 4 describes the forensics im-

plementation and section 5 concludes the paper.

2 RELATED WORK

The ﬁrst layer of our system is the creation of an ad-

hoc network between in-range nodes (vehicles). The

targeted application (i.e. VANET communication) le-

ads to highly dynamic and changing network, mea-

ning a node is expected to connect and disconnect to

a particular network fast. Two cars moving in op-

posite directions at 50km/h, with a WIFI range of

100m leads to a gross calculus of a maximum trans-

mission duration of around 7s. This means that, in

order to connect two nodes, one cannot take longer

than a dozen seconds to perform all connexion stages.

As a consequence, we cannot use the Wiﬁ-Direct op-

tion, available on stock Android smartphone device,

as explained in (Funai et al., 2015). Although too

slow and power consumer, a solution for rooted de-

vice, the Serval project (Serval project, 2016), provi-

des a full mesh network without external hardware.

From the authors’ knowledge, only the GRCBox fra-

mework enables a free and open-source solution for

ad-hoc network deployment for Android devices by

using an external embedded hardware such as a Rasp-

berry Pi. However,they are recent crowd-sourced so-

lution such as GoTenna (GoTenna, 2016) that propose

a proprietary dedicated hardware for mesh networ-

king based radio, which is also under consideration.

Regarding decentralized and distributed device to

device communication, we use the solution proposed

in (Kaul et al., 2017), focusing on efﬁcient and dyna-

mic message dissemination in ITS, to draw our com-

munication model, and avoid the broadcast storm pro-

blem while efﬁciently delivering trafﬁc information

messages. More speciﬁcally to our rerouting appli-

cation, the paper (Leontiadis et al., 2011) proposes

a dynamic routing application. However, they don’t

address the limitations of communication within VA-

NET and they assume the use of external infrastruc-

tures. As a matter of fact, the more recent paper

(Garip et al., 2015) proposes a suboptimal ofﬂine re-

routing solution while addressing the communication

problems that might arise in VANET (i.e. request and

response trafﬁc information approach). Nonetheless,

the security and forensic are not fully addressed.

To the best of our knowledge, although the secu-

rity in VANET is a well researched ﬁeld ((Raya and

Hubaux, 2007)), no paper fully addresses the foren-

sics concern. Often, they assume a third party TA, for

real identity recovery in a secure and anonymous net-

work. However, trusting an external TA would break

the proposed project requirements. This is where

forensics logging comes into play: the whereabouts

and exchanged communication are logged into the

own user’s system while ensuring unforgeability, via

blockchain. For instance, (Sharma et al., 2017) and

(Leiding et al., 2016) use blockchain in VANET. Ho-

wever, they use it for monetary applications such as

an automatic smart contract for insurance or tolling.

The most used blockchain network enabling smart

contract alongside cryptocurrency is Ethereum (see

(Wood, 2014)). However, without the need for a cyp-

tocurrency support (and thus proof of work through

mining), our blockchain can achieve consensus via

randomness and thus avoiding the computational bur-

HACIT: A Privacy Preserving and Low Cost Solution for Dynamic Navigation and Forensics in VANET

455

den of mining. Therefore, our application will be

designed on top of a permissioned blockchain fra-

mework called Hyperledger fabric (linux Foundation,

2016).

3 SYSTEM MODELS

In this section, we present the model design of our

decentralized system.

3.1 High Dynamic Ad-hoc Network

The GRCBox framework installed on a Raspberry Pi

3 model B, with 2 Wiﬁ interfaces, enables ad-hoc net-

working. This architecture allows to communicate

with other cars or with Internet via outer wireless in-

terfaces and to the application via the inner interface

remotely (Wiﬁ). The framework handles the messa-

ges received and forwards them to every outer Wiﬁ

interfaces of nodes connected to the ad-hoc network.

The authors of (Hadiwardoyo et al., 2017) uses the

GRCBox to enable communication between smartp-

hones in order to test a simple application and asses

the performance of the framework. Interestingly, this

system can deliver message at 10HZ, up to 80 meters

and within a delay from 100ms to 900ms. Thus, it

drives us to conclude that this system is really suita-

ble for our practical implementation.

Finally, on the application layer, we propose an

end-to-end secure communication channel and au-

thentication, using the asymmetric cryptography and

the blockchain (see Section 4).

3.2 Scalable Message Dissemination

Each vehicle stores a weighted graph G, containing

for each road segment a weight representing the time

needed to travel this segment and used for ofﬂine

shortest path computation (we are using the well-

known Dijkstra (Dijkstra, 1959) algorithm). Alongs-

ide this graph, it stores also a database DB (as an

Hashmap) containing information measured as the

vehicle goes through roads or resulting from the union

of other nodes databases. Each entry is of the form

< RoadId,AvgSpeed,Timestamp >. RoadId is a uni-

que key, meaning that if a new entry is available for

this RoadId, the system will simply keep the latest

update. AvgSpeed is the measured speed on the road

segment with id RoadId. Finally, Timestamp stores

the time at which the speed sample was measured.

Whenever a vehicle joins a network, it sends its

own DB to every node in the joined network. As a

(a) Step one

(b) Step two

Figure 1: An example of data dissemination.

reply, the receiving nodes send all entries in its da-

tabase whose road ID differs from DB or are simply

more recent.

With this approach, we obtain a simple but efﬁ-

cient 1-hop broadcasting scheme (or also called 1-

persistent broadcast), which avoids the ”broadcast

storm” problem in highly dense trafﬁc. An example of

the dissemination technique for our delay resistant ap-

plication can be found in ﬁgure 1. In subﬁgure 1a, we

observe a car accident at crossroad A, while a green

car comes in the opposite direction and proceeds to

the database exchange with the stopped cars. In sub-

ﬁgure 1b, the green car exchanges its database (with

entry at road AB: AvgSpeed = 0) with the blue car.

Finally, in subﬁgure 1c, the blue car updates its route

and reroutes its course, and thus, it avoids the trafﬁc

jam in crossroad A.

3.3 Dynamic Navigation Rerouting

The rerouting algorithm proposed in this paper uses a

weighting schemes where weights are stored locally

(as a graph) and later used with an optimal shortest

path algorithm.

• Weighting Scheme. We recall that the weights of

the roadmap graph are simply computed as the

time needed to travel the road segment. In other

words, given the road segment between edges i

and j, the weight w

i, j

is computed as w

i, j

with l and v being the road length and average

speed respectively (information available on the

road graph, alongside other parameters such as the

number of lines, type of road etc.). Given a new

estimated average speed ˆv

i, j

measured at time t,

and thus, the corresponding weight ˆw

i, j

, we can

compute the new weight as in Equation 1, with

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

456

being a time threshold after which the data is

considered unreliable.

i, j

= α ·w

i, j

n−1

+(1−α)· ˆw

i, j

with α =

. (1)

• Procedure. Proceed to a simple navigation rerou-

ting, based on newly received trafﬁc information

from other nodes, which can be found in Algo-

rithm 1 and summarized as follow:

After receiving a message containing the gathe-

red road informations db

from another node data-

base, where each entry contains a structure of the

form < RoadID,avgSpeed,Timestamp >, we can

distinguish three steps: The road segment weight

update, the navigation path rerouting computation

and the database update.

1. For each entry in db

, we update the entry of the

weighted graph G corresponding to the same

road segment (i.e. with same RoadId) using

Equation 1. Then, we verify (1) if the diffe-

rence of speed is bigger than a certain threshold

(i.e. T

) (2) if it has not expired regarding a time

threshold T

and (3) if the road segment path list

R contains this road segment and we store the

boolean result in a global variable.

2. If the variable containing the result of the pre-

vious step tests is true, we must recompute the

shortest path (using Djisktra) given the new up-

dated graph G, the current position and the des-

tination (i.e. R.end).

3. Finally, we merge the received set db

to the

database DB containing the trafﬁc information

sent by other nodes and recorded by the user,

keeping only the latest database entry.

The full algorithm is shown in Figure 1.

4 PRIVACY AND FORENSICS

Section 3 presents a simple but complete communi-

cation model in order to connect nodes in communi-

cation range and exchange packet frames. Once the

trafﬁc information is ﬁltered and stored in each node,

it is processed to update road weights in the roadmap

graph used in another run of Dijkstra algorithm to ﬁnd

the new shortest path, if different. However, the in-

novative approach in this work is to tackle the pro-

blem of a complete decentralized and secure system

while enabling forensics capabilities. We can divide

the problem into (1) the forensics system and (2) the

security between nodes.

Algorithm 1: Navigation rerouting algorithm upon newly

received trafﬁc information.

Require: db

,R, G.

1: db

is a list containing a structure of

[RoadID,AvgSpeed,Timestamp], R and G

are the road path list and the road weighted graph

respectively

2: function PATH REROUTING(db

,R, G)

3: isModi f y ← False

4: for d in db

5: t ← d.Timestamp

6: w ← G[d.RoadID].Weight

7: v ← d.AvgSpeed  New speed

8: n ← current time

9: G[d.RoadID] ← Updated as Eq 1

10: isModi f y ← ||w −v|| ≤ T

& ||t −n|| ≤ T

& R.contains(d.RoadId)

11: if isModify then

12: R ← DJISKT RA(G, position,R.end)

13: DB ← DB ∪ db

 Keep latest information

4.1 Forgery-proof Logging with

Blockchain

Our original contribution to the VANET community

is to propose a decentralized VANET communication

network without relying on any external third party.

However, the need to provide evidence might arise,

such as in car accident or police investigation (eg: a

previous car behavior), in order to establish respon-

sibilities. Due to the problem speciﬁcation, such as

low bandwidth capacity and computation on a mo-

bile device, we propose that the user stores in its lo-

cal storage blocks containing logged data and chained

with hash, similar to a blockchain. The hashing of

data guarantee a one way encryption function, mea-

ning it guarantees that the data is untouched as long

as the hash submitted is immutable. Obviously, we

don’t want the user to modify this data considering

forensics fraud. Therefore, we propose the following

scheme (see Figure 2):

• A user initially submits a smart contract (also

known as chaincode) similar to Figure 3, which

contains a public hash, the owner public key and

a public function executable only by the owner

of the Smart Contract, to the network consensus.

This step is used as registration. In other words,

once the smart contract code is compiled, the new

user needs to submit the contract to the other peers

for consensus. Upon validation, the user recei-

ved a conﬁrmation alongside the hash of his smart

HACIT: A Privacy Preserving and Low Cost Solution for Dynamic Navigation and Forensics in VANET

457

Figure 2: Blockchain pipeline.

contract (i.e. contract index). An example of an

initiating smart contract is shown in 3.

• While the user uses the system, it logs a block of

data (i.e. an ofﬂine loop) and computes the hash

of the newest block (i.e.: blockchain).

• When user connect to network, it sends the latest

hash alongside its database (road informations).

The receiving node returns its database as well as

the signed hash.

• When a user is connected to the Internet through

Wi-Fi or cellular network, it executes the setHash

function given its accumulated hash (signed or

not).

• Every time a node is connected to the network, it

downloads the new blockchain version (i.e. up-to-

co n tr a ct Cli e nt_ c on t r ac t {

by te s [] has he s = ne w b yte s ( ) [ N ];

ui nt [] v a li d at i on = n ew uint () [N ];

ad d re ss pub li c pk = m sg . s en de r ;

fct s e th a sh ( b yt es [] sH ash , ad d re ss [ ] oPk ) {

re q ui re ( m sg . s en de r == this . pk )

;

re q ui re ( sH as h . le ngt h == o Pk . l en gt h );

for ( u in t i = 0; i < sH as h . len gt h ; i ++)

{

# no v a li d at i on for t hi s ha sh

if ( oPK [i] = = n ul l )

{

th is . h as hes . push ( h );

th is . v a li d at i on . p us h

(0) ;

}

el se {

by te [] h = S H A2 56 ( s Has h [ i ]) ;

by te [] h_ p = de c ry pt ( de cry pt (

sH as h [ i ] , o Pk [ i ] ) , pk ) ;

if ( h == h_p ) pu sh ( s Ha sh [ i ])

}

fct p us h ( by te s a )

{

if ( ha she s . co n ta i ns ( a )){

ui nt i = has he s . i nd exo f ( a ) ;

ha she s [ i ] = a ;

val ida t io n s [ i ] += 1;

}

el se {

ha she s . pu sh (a ) ;

val ida t io n s . pu sh ( 1) ;

}

Figure 3: Proposed initial Ethereum smart contract.

date hash from all nodes).

This scheme allows the user to verify, using the

hash available in his or her smart contract, that the

data he/she owns is valid and not modiﬁed. Moreover,

encountered nodes sign the latest hash which add a

layer of proof. A legal entity can then check the ledger

to see which submitted hash were endorsed by other

peers.

Without the need for cryptocurrency (i.e. mone-

tary scheme), the proof-of-work is useless here. Thus,

the Byzantine fault tolerance consensus algorithm can

be achieved by simply selecting a peer ”leader” rand-

omly, which will gather all transactions (smart con-

tract execution) into a new block. As for most block-

chain technologies, this block is then broadcast even-

tually to all peers with the gossip protocol. Therefore,

such a consensus algorithm is considered computati-

onally easy and suitable for mobile application.

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

458

4.2 Inter-node Security

Furthermore, thanks to the scheme, a receiver node

can retrieve the public key of the sender, given the

sender’s smart contract index (i.e. smart contract hash

address, considers unique among users and serving to

identify), in the blockchain. An example of the ex-

change can be seen in ﬁgure 4. In other words, we

guarantee that the data remains conﬁdential within

our service users (i.e. valid registered users) while

being able to authenticate data and avoid malicious in-

jection, by means of asymmetric encryption. Indeed,

a node A maintains an up-to-date and encrypted ver-

sion of its database C, containing gathered road trafﬁc

informations, using his private key K

. This encryp-

ted data is computed at a certain interval considering

a trade-off between power consumption and the no-

velty of the data. Once it has joined a new network,

it broadcasts its encrypted data C to all other nodes

alongside its blockchain identity H

c (smart contract

address). Upon message reception, the receiving node

uses the unencrypted A’s id H

c to retrieve A’s public

key PK

into the permissioned ledger that it has alre-

ady access to. If no address matches or no public key

available, A is not registered to the network. Other-

wise, we use PK

to decrypt C. A challenge is ﬁnally

tested to assess the validity of the data.

By means of blockchain functionalities, we pro-

pose a simple way of encryption and authentication.

Of course, it implies that the user needs to have an up-

to-date ledger in order to acknowledge newly subscri-

bed users and retrieve their public key, and that the

data is secured and validated by the community.

4.3 Malicious Node

The proposed model allows for an user to prove the

data provenance and its integrity based on validation

from other encountered users. However, a malicious

node could alter its data while still submitting a valid

hash. Thus, we propose the following extensions:

• White-box cryptography (Brecht, 2012): While

this ﬁeld is still under study, it proposes a way

to hide private key into the source code. In our

case, it could secure the submitted hash, providing

a way for any nodes to verify that it has been sig-

ned by our application (and not submitted a pos-

teriori).

• 50% overlaps: Each block of data will overlaps

each other, to help check data consistency at the

forensics level.

However, the incentive to submit as much valid

and correct hashes as possible in order to protect an

Figure 4: Authentication and privacy pipeline.

user legally will eventually protect against malicious

behaviour.

5 CONCLUSION

Using well known techniques, we propose a real-time

low-cost system able to connect vehicles in a network

and exchange trafﬁc information in order to dynami-

cally reroute user to spare overall time. With a sim-

ple raspberry pi and GRCbox framework, we enable

multi interface short-range communications through

an ad-Hoc network. We opt for a simple 1-hop bro-

adcasting technique in order to share each node da-

tabase, containing the accumulated trafﬁc knowledge.

Once new information is received about road segment

on the navigational path, one can use our simple but

yet efﬁcient re-routing algorithm.

The dynamic routing application in this project is

the test-bed for our innovative contribution: we pro-

HACIT: A Privacy Preserving and Low Cost Solution for Dynamic Navigation and Forensics in VANET

459

pose a complete and secure decentralized system that

preserves user privacy, since no computation is made

at a central server that could gather private data. In

addition, the system enables forensics logging within

a network made exclusively of mobile device: by use

of data hash stored in a lightweight distributed block-

chain through Smart Contract, we allow each user to

be able to prove its own data integrity. Furthermore,

this blockchain is used to retrieve nodes public keys

and thus, authenticate and decrypt packets.

5.1 Discussion on Mobile Computing

Given the nature of our proposed solution, we foresee

several problems and points that must be addressed:

• Given the communication window between no-

des, the data exchanged must be as concise as pos-

sible. To that end, we can prune the data to be

shared to include only latest gathered road infor-

mation.

• The computation of the navigation path might be

a challenge for mobile devices. However, nowa-

days smartphones can handle heavy computatio-

nal burden and ﬁnding the shortest path is a well

known and optimized algorithm.

• We encrypt the data exchange between nodes with

asymmetric encryption. Such encryption is howe-

ver computationally expensive.

• The frequency of smart contract execution and

ledger update will directly affect the efﬁciency of

the proposed solution but also the power and com-

putational requirements.

5.2 Future Work

The solution that we propose is yet to be developed.

The work will question different sensitive subjects

speciﬁc to VANET limitations. For instance, we fo-

resee that the size of the database to be shared over a

really small amount of time might be a challenge, or

that the computing power available on mobile device

might not be sufﬁcient to run Dijkstra or asymme-

tric cryptography. The latest project break-in invol-

ves a complete different approach. Indeed, the hyper-

ledger Fabric functionalities allow to store key/value

pairs alongside the ledger of transactions as permissi-

oned blockchain. A user would then directly submit

a new transaction containing a new measured speed

for a certain road id as transaction. Such scheme ena-

ble forensics through transactions crawling and dyn-

amic rerouting through database query. However, the

scalability of the system for mobile application is yet

unknown.

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