NiPA: Nicknames Pool Approach for Protecting Privacy of User Data in
Intelligent Transportation Systems
YAZED ALSAAWY, AHMAD ALKHODRE, ADNAN ABI SEN
Department of Computer Science
Faculty of Computer and Information Systems
Islamic University of Madinah
Madinah 42351
SAUDI ARABIA
Abstract: - The intelligent transportation system has made a huge leap in the level of human services, which has
had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source
of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic
contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot
be used with many services because of their impact on the accuracy of the service provided itself, they depend
on changing the number of vehicles or their physical locations. This research presents a new approach based on
the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users
permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the
nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud
as well as distributed sub-pools in fog nodes to avoid intelligent delays and overloading of the central architecture.
Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and
its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely
on the well-known privacy criteria: Entropy, Ubiquity, and Performance.
Key-Words: - privacy, intelligent transport systems, multi-layered comprehensive approach
Received: August 23, 2021. Revised: April 17, 2022. Accepted: May 16, 2022. Published: June 8, 2022.
1 Introduction
The problem of traffic congestion is one of the
problems most concerned with governments because
of its impact on all aspects of life in cities and the
performance of businesses [1, 2]. In addition, it is
closely related to the high rate of accidents and
deaths, as well as the issue of environmental
pollution and waste of fuel and time [3, 4]. With the
advent of the Internet of Things and related
technologies [5] such as cloud computing [6], fog
computing [7], wireless sensor networks (WSNs) [8],
radio frequency identifiers (RFID) [9], and other
smart things that can be communicated and
controlled from anywhere over the network. Many
solutions [10] to this problem have emerged that are
different from traditional solutions, which may be
very expensive or not possible in some places [11],
such as building bridges and tunnels [12], or those
that are not as effective as traditional traffic lights,
which may cause more congestion at peak times [13].
Through our previous work [14], we proposed a
multi-layered comprehensive approach (MLCA) to
solving the traffic problem in the Kingdom of Saudi
Arabia - Medina. The MLCA is articulated on a set
of layers that are integrated with each other to give a
comprehensive, homogeneous, and uninterrupted
solution. This solution is not affected by weather and
ambient conditions. It can work in all contexts to
provide an outstanding service. This solution was
based on a range of services starting from smart
vehicles [15, 16], smart traffic lights [17], smart
roads [18], and a large number of smart applications
such as the smart parking application and the
emergency application [19, 20]. All of these solutions
were based in a simplified way on processing images
captured by sensors, drones, and cameras implanted
in smart signals. On the other hand, information
collected from social media and historical
information about road conditions, people, and
vehicles [21]. All these with each other to reach an
adaptive optimal solution.
On the other hand, vehicle pioneers have many
applications on their mobile phones, such as Google
and its family, navigation applications [22], shopping
and tracking applications [23], social networking
applications, school applications [24], and other
applications that pertain to different daily user
segments. As a result, for all previous smart solutions
and applications, a huge amount of data is generated
and flows between users and services providers [25,
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26]. Unfortunately, this data has caused big problems
regarding the security and privacy of users [27, 28]
including the two types of attacks Active attack such
as the denial of service, and the Passive attack that is
eavesdropping, obtaining, and exploiting users’
information to reveal their personal and private data
[29, 30]. For that, most people and governments
became aware of the importance of the privacy and
security of data [31], where the attackers can know
everything about users and their movements, jobs,
homes, hopes, tastes, trends, diseases, and financial
incomes, etc.[32]. This has led many to refrain from
using smart applications and avoiding dealing with
smart objects to prevent infringement of their privacy
[33].
Government and researchers considered privacy
and security as the most challenging for new
technologies like IoT. So, many of country put a new
laws to enforce companies to preserve the privacy
and security of their customers [34]. General Data
Protection Regulation (GDPR) [35] is a European
law for preserving privacy, it is announced in 2016,
and started applying in 2018. Actually, the law
solution is not enough to ensure privacy and security
[36]. Also, the several protection solutions to the
problem of Location-based privacy that have been
proposed by researchers, including [37]: Dummy,
obfuscation, peers cooperation, etc. are not suitable
or compatible with new smart services like the traffic
management. Where most of these methods affect
adversely the quality of the main service and the
accuracy of its functionality, (more detail will be in
the previous works section, where we will discuss
these methods and their negatives).
Therefore, the contributions of this research are:
Review previous approaches to privacy
and mentions the disadvantages of these
approaches
Propose a new approach for preserving
the privacy of users in the location-based
services called the "Nicknames Pool
Approach".
Present a new approach that solves all the
cons in the previous methods with highly
compatible with the nature of location-
based services.
Proof by simulation of the superiority of
the proposed approach according to
privacy and performance metrics.
2 Previous works
The problem, of data privacy and data security,
has become a sensitive issue and many countries have
begun to regulate special laws and policies that
companies and service providers must adhere to
ensure the security and privacy of users [37, 38].
Unfortunately, the governmental laws, despite their
importance, are insufficient and do not guarantee the
service provider’s commitment, and on another side,
they are insufficient to deal with malicious actors or
external attackers on the other [36 - 38].
Privacy [39] is the right of each person to have full
access to their data and the ability to decide who,
when, why, how, and whether their data will be used
by the service provider. To protect privacy,
anonymity must be ensured in applications or public
services. In addition, it is not enabling the attacker to
create a profiling file for each user through which
queries are linked to their users. Finally, preventing
the user from being tracked and knowing his real
location at a specific time. Thus, since data security
[40] is concerned with protecting the confidentiality,
integrity, and availability of data, we find that privacy
faces a greater challenge because the user sends his
data to the service provider, who can himself be a
source of threat to the user, such as being malicious
or being hacked by an attacker or a repairer of
servers. For more details about the difference
between security and privacy, see [41].
Therefore, during the past years, many techniques
for protecting privacy have appeared, and the table 1
shows the most common techniques:
TABLE 1: THE MOST COMMON TECHNIQUES FOR PROTECTING PRIVACY
PRIVACY TECH
WORKING MECHANISM
Processing Data [42]
Deleting or collecting private data before
sending it to the service provider.
Anonymity [43]
Use a pseudonym or hash code to hide the real
name
Mix-zone [44]
Improving the previous technique by
changing the pseudonym every period of time
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Third Trusted Party
(TTP) [45]
Anonymizing the user by relying on a trusted
third party
Obfuscation [46]
Add obfuscation to the data in order to protect
the original data
Peers Cooperation
[47]
Collaboration among the users themselves
misleads the service provider
Cloak Area [48]
It relies on a distributed TTP in cells so that
each TTP is responsible for hiding the identity
of users within its own cell
Private Information
Retrieval (PIR) [49]
Pulling a large amount of data from the
service provider and storing it locally for later
use
Dummy [50]
Sending a mixed package of fake and real
inquiries to the service provider
Anonymity-based methods are best suited to
location-based applications and services [51]. Thus,
it is better for most of the applications and services of
the intelligent transportation sector. This is because
other methods affect the accuracy of the basic service
(for example the calculation of congestion rates in a
specific area, the accuracy of a shipment receipt for a
specific location, or reaching a specified target). That
is meant it affects the accuracy of the main service
and its results. However, the traditional methods in
the previous table that use the concept of anonymity
(Anonymity, Mix-zone, and TTP) have many
drawbacks, such as ineffectiveness in protection, ease
of detection, or the need to trust a third party [51, 52].
To deal with the previous problems, researchers at
[51] have relied on fog nodes in smart cities to play
the role of managing cooperation between two
vehicles by switching alias between them in order to
mislead the service provider and prevent it from
drawing a correct tracking path for each. However,
the disadvantage of this method is that it only works
in the case of intersections in addition to the presence
of detection of part of its data (its exact location)
during the swap. This is due to the two exchanged
vehicles being in the same location as the protection
service provider. Therefore, if there are several
malicious fog nodes, the user will be at risk of attack
tracking the areas.
The Centralized pseudonym-changing scheme
[52] is similar to the previous method but it is
centralized rather than distributed. This method
requires the user to send their exact location to the
central protector, which then replaces their nickname
with that of another nearby user. The problem of this
approach is that it depends on absolute centralization,
which is lead to a server bottleneck problem. In
addition, this approach requires the user to send his
accurate and trusted location, and therefore the server
itself may be a source of danger to the user, whether
it is malicious or hacked. Finally, when exchanging
with a user in the same location, this leads to the
disclosure of accurate information to the protector
service provider (such as the exact geographical
location of both users).
Our proposed alternative solution is a mixture
between central and distributed. We relied on the fog
node to relieve the load on the central server while
keeping the option of direct contact with the central
protection provider available in case the user did not
trust the fog node, in order to break the fog-mix-zone
tracking attack. The proposed approach does not
require the user to send their query or their exact
location to the security provider (whether the fog
node or the central server). Therefore, the proposed
approach will not pose any threat to the user's
privacy. The user only sends him the area he is in,
and he sends him a nickname that has been used by
others in the same area or a neighboring area. Finally,
the algorithm of the proposed approach uses the same
name for K number of users and not just swapping
between two users only, which means hiding the
identity between K users. This means a higher rate of
privacy and more difficulty for attackers to jailbreak,
all without a noticeable impact on performance.
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Moreover, most importantly, it should not have any
impact on the main service based on the location.
3 The proposed Solution
The idea of this approach is to mislead the attacker
even if it is the service provider himself, by using the
same aliases between different users and in different
places. Thus, the formation of a misleading database
of the service provider that it cannot use to analyze a
particular user's queries in order to disclose
additional information about it and to penetrate its
privacy.
To achieve this, it was suggested that relying on a
third party responsible only for tracking the
nicknames used by users in different regions without
knowing any details about users' data, queries, or
destinations. We create a central database of
nicknames used in each area during an earlier period,
and the user is given a specific, not random,
nickname based on his current area without
disclosing his real location or destination.
The suggested nickname is a nickname used in an
early period and one of the adjacent areas to the
current user's area. Therefore, the service provider
will consider that the user of that nickname still uses
the same nickname and that he has moved to a closed
area. It will therefore store fake information about
both users. The complexity and distortion of the
service provider will be increased as the number of
users of the proposed approach increases. The
nicknames are exchanged periodically between each
other without the need for cooperation directly.
To solve the bottleneck problem and prevent
overloading the cloud that contains the central
database or Pool-of-Nicknames, we suggest
benefiting from the fog infrastructure distributed at
the edge of the end-user network. The cloud
distributes a part of data to each fog node (containing
nicknames used during the previous period within the
nodes adjacent to the node itself).
The fog node has two caches (primary with big
size and secondary with small size). The fog stores
these nicknames within the primary cache, and once
a new request from a user is raised within the area,
one of the nicknames is given and that nickname is
stored in the secondary cache.
At each synchronization period (X), each fog node
will send its second cache content (its nicknames
used within the X period) to the Central Pool cloud.
At the same time, the fog node receives a list of
nicknames used within the neighbor nodes to store in
the primary cache.
FIGURE 1. NIPA: NICKNAMES POOL APPROACH FOR PROTECTING PRIVACY,
Fig 1: represents the proposed approach. The
approach consists of the users, fog nodes, and the
cloud containing and managing the nicknames pool.
Operationally at the beginning of the command and
before the service request process begins with the
service provider, the Nickname is obtained from the
fog node and then the request is sent. On the other
hand, the system develops and updates Nicknames,
as shown in Fig 1, so that the used nicknames are sent
to the cloud, which in turn sends the nicknames used
in the neighboring nodes. The synchronizer in the
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cloud manages the exchange of Nicknames between
fog nodes.
3.1 Suggested policy features:
1. The user does not need to trust any third
party. This means that the user does not
provide the fog or the cloud any information
about his exact location, the direction of
movement, or query.
2. The user does not need to cooperate with
other users.
3. Great misinformation to service providers in
such a way that it is difficult for them to form
a true profile of each user or link each
Nickname to their real user. Thus, the level
of privacy has been significantly improved
compared to previous methods that have
used the principle of anonymity by
Nicknames.
4. Improve system performance by employing
the huge number of fog nodes in smart city
environments
5. Relieve load for a user who will only have to
use a Nickname without having to generate a
jamming area or generate a dummy.
6. The proposed approach does not affect the
performance of smart traffic systems that
rely on the information on the number of
vehicles in a particular area. Obfuscation or
Dummy are not used.
4 Result and discussion
4.1 PRELIMINARIES
This section provides proof of the effectiveness of
the proposed NiPA approach compared to previous
methods (FM-ZA, Mix-Zone, and Anonymity) that
have also relied on Nickname as its own protection
principle. A simulation of the working mechanism of
each of the previous methods was implemented based
on the visual environment Studio.Net according to
the following assumptions [51]:
1. Divide the study area into 100 * 100 cells, equal
in size
2. 10,000 users randomly distributed over cells
3. 100 different points of interest
4. 4G as a network between users and Anonymizer
or Fog
5. Internet Connection with the Service Provider
6. The service provider is a malicious attacker that
seeks to collect information about users to breach
their privacy
To compare all methods the following criteria have
also been used [53, 54, 55, and 56]:
Entropy [53], which is the main method for
measuring the level of privacy, as it represents the
amount of correct information collected by the
service provider, which can be associated with the
user. In other words, the privacy entropy represents
the uncertainty rate of the service provider. The
entropy is given by the following equation:
E =  󰇛󰇜
 (1)
Where Pi is the probability of that query belongs to
submitted Peer Pi=1 E=0
Therefore, the best value for the Entropy is E equals
to one, i.e. absolute uncertainty for the attacker.
Estimate Error [54]: it is the error rate on the
attacker's side, which means the ratio of linking
information by a specific user, and it represents the
privacy violation rate:
  (2)
K-Anonymity [55]: A simple measurement
represents the percentage of collected user queries by
the service provider or attacker.

(3)
Where : is the number of queries related to a user
and is the number of queries, which is not related
to a user.
Cost & Performance [56]: The volume of data or the
number of queries sent to the service provider against
the number and size of the real query, as well as the
response time of the query.
There are also other non-quantitative criteria,
the most important of which is the immunity of
approaches against certain attacks and the need to
trust a particular party or not.
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In the following, we present simulation results
according to the above-mentioned hypotheses and
based on the criteria for comparison:
4.2 Performance analysis
Fig 2 shows that both NiPA and FM-ZA
approaches achieve the highest entropy rate due to
the number of times the nickname is used. On the
other hand, since the nickname is already used, this
further misleads the service provider, thus collecting
false data and continuing to protect privacy.
Figure 2. A performance analysis Comparison to the
number of queries sent by the NiPA, FM-ZA,
Anonymity and Mix-Zone
In the traditional Anonymity approach that
has the lowest level of protection, we notice that with
the time and increase in the number of queries that
use the same nickname, the service provider can
collect the queries and relate them to one user. After
analysing those queries, there will be a breach of the
user's privacy. The Mix-Zone approach has improved
the protection of the Anonymity approach by
changing the nickname when entering each new
region. Nevertheless, according to the time tracking,
the service provider can also hack it and record
information about the real user. Similarly, NiPA and
FM-ZA will achieve a maximum listen rate of 100%,
and K-Anonymity is always equal to zero.
In Fig 3, it is clear that both approaches NiPA
and FM-ZA are worse than Mix-Zone, while the
Anonymity is better. It is rational because anonymity
only needs the user to generate a nickname once for
the first time. While in the Mix-Zone, the user needs
to regenerate at each entry to a new cell, adversely
affecting the level of protection achieved.
Figure 3. An entropy Comparison by the NiPA, FM-
ZA, Anonymity and Mix-Zone.
In the FM-ZA and NiPA approaches, the user
connects to the fog node or nickname pool server (in
the cloud) to get a nickname at each query. However,
it can be noted that the delay effect is very simple, as
the nickname’s selection process is not compared
with other methods such as generating a wide range
of fake queries as in Dummy, or Query obfuscation
and creating a locked area as in Obfuscation [3] or
Cloak Area [13], which is one of the famous
protection methods used. NiPA and FM-ZA do not
affect the size of the query or the number of queries
sent, which means effective protection with a
neglected effect on the response time.
More, NiPA and FM-ZA can outperform Mix-
Zone or Anonymity in response time if they relied
upon a cache in the fog nodes. However, this means
the need to trust the fog node itself and the user
should send his query also to the fog node and not
only its direction or area.
4.3 The superiority of NiPA over FM-ZA
We have noticed through the previous results the
convergence in the results between both approaches,
but what the reason for the superiority of NiPA is.
The NiPA approach is superior to the other
approaches in three important points:
1- Applicability and service availability: In the
NiPA approach, protection can be applied
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anywhere by simply contacting the pool,
while in FM-ZA it is intended to work when
there are intersections to change directions
between users when changing names among
them.
2- The second and most dangerous point is that
the FM-ZA approach indirectly exposes the
user’s area since all the cooperative users are
at the same point (at the intersection), which
means that the user’s site is not well
protected as well as not immune to a path
tracing attack, or a tracing attack User's area.
As for the NiPA approach, it exchanges
nicknames with other users in neighboring
regions, that is, the region is completely
changed, thus protecting the exact location of
all users.
Finally, fog nodes pose a danger to users of the
FM-ZA approach if they are malicious because of the
necessity of contacting them. Nevertheless, in the
NiPA approach, users, in some cells, can
communicate with Nickname Pool Server directly.
This could completely prevent the zone tracking
attack this is a suffering point for the FM-ZA
approach.
5 Conclusion
In this paper, we presented a new approach
based on the use of smart nicknames. This approach
maintains the quality of service and prevents users
from permanently tracking them, compromising their
privacy, revealing their whereabouts, or discovering
additional details about their behaviour and
movements. Our new approach relied on creating a
central pool of aliases in the cloud as well as
distributed sub-pools in fog nodes to avoid intelligent
delays and overload of the central architecture.
Finally, through simulation and discussion on actual
examples, the proposed approach excels and adapts
to new services and provides an effective level of
protection. For the comparison, we relied on well-
known privacy criteria: Entropy, Ubiquity, and
Performance.
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Funding
The Deanship of Scientific Research, Islamic
University of Madinah, Saudi Arabia, funded this
research under Tamayuz research grant number
2/710.
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