A NEW FORCE IN THE DIGITAL ECONOMY: DIGITAL TWINS APPLICATIONS AND CHALLENGES

Abstract

Digital twin technology is revolutionizing the digital economy by merging the physical and virtual worlds, making it an essential for digitizing industries. A digital twin (DT), a virtual replica of a physical object, system, or process, is anticipated to create an intelligent, predictive, and highly efficient economy. There is an increasing demand for novel developments in DT across a variety of industries, including manufacturing, construction, oil and gas, aerospace, energy, and healthcare. Certain stakeholders are already realizing that DTs not only enhance efficiency and reduce costs but also enable the creation of new service offerings. However, the adoption of DT brings along a number of challenges, including concerns about data privacy and security. DT has become a popular topic with increasing interest in academic journal articles and solution offers from the industrial sector. This study presents a literature overview of DT in the context of privacy and security issues to gain a better understanding of the key barriers that may impact the future adoption of DT technologies. The paper presents an analysis of articles published in Scopus, Web of Science, and IEEE Xplore databases between 2019 and 2024 that examine the privacy and security problems of DT.

Full text

A NEW FORCE IN THE DIGITAL ECONOMY:
DIGITAL TWINS APPLICATIONS AND
CHALLENGES
MARIJA KUŠTELEGA
Faculty of Organization and Informatics
University of Zagreb
Pavlinska 2, 42000 Varaždin, Croatia
marija.kustelega@foi.unizg.hr
RENATA MEKOVEC
Faculty of Organization and Informatics
University of Zagreb
Pavlinska 2, 42000 Varaždin, Croatia
renata.mekovec@foi.unizg.hr
AHMED SHAREEF
Faculty of Organization and Informatics
University of Zagreb
Pavlinska 2, 42000 Varaždin, Croatia
ahmed.shareef@foi.unizg.hr
ABSTRACT
Digital twin technology is revolutionizing the digital economy by merging the physical and
virtual worlds, making it an essential for digitizing industries. A digital twin (DT), a virtual
replica of a physical object, system, or process, is anticipated to create an intelligent,
predictive, and highly efficient economy. There is an increasing demand for novel
developments in DT across a variety of industries, including manufacturing, construction,
oil and gas, aerospace, energy, and healthcare. Certain stakeholders are already realizing
that DTs not only enhance efficiency and reduce costs but also enable the creation of new
service offerings. However, the adoption of DT brings along a number of challenges,
including concerns about data privacy and security. DT has become a popular topic with
increasing interest in academic journal articles and solution offers from the industrial
sector. This study presents a literature overview of DT in the context of privacy and security
issues to gain a better understanding of the key barriers that may impact the future adoption
of DT technologies. The paper presents an analysis of articles published in Scopus, Web of
Science, and IEEE Xplore databases between 2019 and 2024 that examine the privacy and
security problems of DT.
KEYWORDS: digital twin, digital economy, key challenges, privacy, security, The
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)

1. INTRODUCTION
Digital twin (DT) technology is revolutionizing the digital economy by seamlessly offering
a virtual blueprint of physical processes throughout the entire business lifecycle, enhancing
the efficiency and effectiveness of processes, products, and services [Singh et al., 2023]. DT
is a virtual replica of physical objects, processes, and services. It simulates the physical
counterpart, enabling enhanced predictive capabilities and operational efficiency throughout
various digital economies [Pervez et al., 2023]. According to Martinescu [2023], there are
three different types of DT: digital model, digital shadow, and digital twin, each serving
distinct functions and purposes. A digital model represents a predictive model of a physical
counterpart without live updates or data exchange. Digital shadows are virtual models
updated with data from the physical model, while digital twins are virtual models that
communicate bidirectionally with their physical counterparts.
The global DT market is growing at a compound annual growth rate (CAGR) of 38.2%
and is expected to reach a value of $26.07 billion by 2025 [Lee et al., 2020]. Market statistics
indicate an increment of $48.2 billion by 2026 [Böhm et al., 2021]. It plays an important
role in the digital economy by providing virtual representations of physical assets, enabling
real-time monitoring, predictive analysis, and simulations [Yi, 2023; Li et al., 2022;
Clementson et al., 2021]. Although it was initially developed to improve manufacturing, DT
has been expanded into various domains, from traffic lights to smart cities and agriculture
to healthcare [Araújo et al., 2022; Pervez et al., 2023], all of which contribute to the digital
economy. DT plays a significant role in predictive maintenance in manufacturing industry
[Böhm et al., 2021]. This leads to cost reduction and process optimization by allowing
designers and engineers to work on deep detail of the product via a virtual model before
initiation of the physical product [Chen et al., 2023]. It not only reduces cost but also
improves manufacturing productivity and efficiency, leading to enhancement in designing
and manufacturing processes of physical products. However, the adaptation of DT is not
without challenges. According to Yi [2023], there are unique challenges of privacy risks, for
which the author demonstrates secure ways to provide services. Afzal et al. [2023] stressed
the need for reliable bi-directional communication in DT to establish data integrity, required
for practical decision-making, and protection of privacy against cybersecurity threats with a
strong focus on security and quality, ensuring integrity and reliability. Additionally, the need
for privacy-preserving networks, security protocols, and governance frameworks is stressed
to protect sensitive information and ensure compliance with data regulations [Yi, 2023].
The digital economy is growing rapidly via the use of new technologies that increase
connection, facilitate automation, promote data analysis, and offer new commercial
opportunities. In today's industrial scene, DT has emerged as a vital innovation, redefining
the operational, strategic, and economic paradigms of enterprises across several industries.
This technology, which generates a virtual reproduction of actual assets, processes, or
systems, has the potential to dramatically improve efficiency, save operational costs, and
open up new revenue sources. The purpose of this study is to provide a comprehensive
literature review on the privacy and security challenges of DT technology, aiming to identify
potential barriers to their future industry adoption. The main research questions (RQs) were:
• RQ1: What are the primary application domains of DT technology?
• RQ2: Which privacy and security challenges arise most frequently when using
DT technology?
This study is unique in a literature review on a wider range of privacy and security
challenges, taking into account both technical and non-technical issues. The work is

structured as follows: Section 2 describes the methodology used; Section 3 presents the
literature review on DT applications; Section 4 shows the main results; Section 5 discusses
the main privacy and security challenges; and in Section 6, the paper is concluded.
2. METHODOLOGY FOR LITERATURE REVIEW
PRISMA methodology was used to perform the literature review [Moher et al., 2009]. To
identify relevant articles for the research area, a search string ("digital twin" OR "digital
twins") AND ("privacy" OR "security") in the titles or abstract of the paper was utilized. A
total of 438 articles were found, with 282 articles remaining after removing duplicates. All
publications that dealt with privacy or security challenges in the context of DT were
included; all other articles that primarily addressed subjects unrelated to the actual
implementation and challenges of DT were removed. After implementing the inclusion and
exclusion criteria, a total of 49 articles remained for further analysis. Only English-language
articles released between 2019 and 2024 were considered. Furthermore, to better understand
the concept and structure of the selected articles, keyword co-occurrence analysis was used
to investigate the link between keywords in the literature. It illustrates how specific terms or
keywords frequently appear together in text data, with nodes representing authors keywords
identified in journal articles and linkages representing word co-occurrences [Radhakrishnan
et al., 2017]. This analysis was performed using the bibliometrics library from the R tool,
on 282 articles selected in the first step of the PRISMA approach.
3. DIGITAL TWIN APPLICATIONS
DT have applications in various domains where they can serve as a factor that will create a
competitive advantage. Despite significant investments in Industry 4.0, industry is not yet
capable of fully utilizing new technologies [Mantravadi et al., 2023].
3.1. CONSTRUCTION
In the construction, the main applications are related to smart cities development and
surveillance of building projects [Waqar et al., 2023]. DTs are vital for urban planning,
specifically for their visualization and simulation capabilities [Lei et al., 2023]. By
incorporating the entire ecosystem in decision-making through open innovation and citizen
engagement, these can produce co-innovations [D’Hauwers et al., 2021]. Weber-Lewerenz
[2021] believes that DT in construction projects will have corporate digital responsibility
built into them. The use of emerging technologies like blockchain and non-fungible token
(NFT) standards could improve secure data sharing [Teisserenc & Sepasgozar, 2022].
3.2. INFRASTRUCTURE
Other DT applications include facility and infrastructure management also essential for
preserving safety and functionality, with smart infrastructure emerging alongside traditional
infrastructure assets. They could be used in civil infrastructure systems for transportation,
energy, water and waste applications such as demand forecasting, emergency planning,
predictive maintenance, security resilience, and so on [Callcut et al., 2021]. For example,
using bridge digital twins’ models can provide effective remote management such as bridge
model updating, monitoring, operational and other maintenance purposes [Ye et al., 2022].
They can be used for asset management and as a way to improve maintenance practices,
service delivery, and sustainability [Fialho et al., 2022].

3.3. MANUFACTURING
In the manufacturing industry, the digital twin can be used to facilitate new business creation
[Timperi et al., 2023]. An interview study with eight manufacturing companies identified
barriers and challenges for fully leveraging DT benefits in an industrial context [Wärmefjord
et al., 2020]. The study revealed a significant gap between academia and industry, with
challenges primarily observed in system and work process simulation, management issues,
and appropriate education. This is supported by Neto et al. [2020], who claim that
standardization, technological maturity, and integration, as well as lack of people's
qualifications and resistance to change impede the use of DT.
3.4. HEALTHCARE
In healthcare, DT could be an effective tool for short clinical trials and providing preventive
healthcare to enable personalized medicine [De Maeyer and Markopoulos, 2021]. In his
study, de Boer et al. [2022] explores the various ways in which DT can be integrated into
people's lives, focusing on how potential users want to be treated and how this can be applied
to the introduction of DT into care practice. Similarly, Popa et al. [2021] investigated the
socio-ethical benefits and risks of DT in healthcare, focusing on the prominent risks
triggered by their adoption and perceived stakeholders’ benefits.
4. RESULTS
This study examined 49 articles in which challenges can roughly be divided into technical
and non-technical challenges. Figure 1 depicts the frequency of identified privacy and
security challenges, divided into four categories: (1) privacy utilization, (2) regulatory
compliance, (3) privacy risks, and (4) security risks.
Figure 1. Frequency of mentioned privacy and security challenges
Source: Authors
Figure 1 illustrates the prevalence of security risks, including those related to system
vulnerabilities, attacks, authentication, and overall threats to the data confidentiality, integrity,
or availability. Next are those related to the utilization of privacy (privacy risk), such how

private and sensitive information are handled and shared. Regulatory compliance-related
challenges like data governance, ownership, intellectual property, and standardization are
almost equally represented. When everything is taken into account, the challenges resulting
from the real violation of privacy in specific attack or data breach scenarios are not that
concerning.
Figure 2 depicts the co-occurrence analysis of terms performed in the R tool on 282
articles found in the IEEE Xplore, WoS and Scopus databases. With the help of keyword
co-occurrence analysis, it was possible to see which terms are most often mentioned together
in the literature.
Figure 2. Keyword co-occurrence network in the R tool
Source: Authors
In Figure 2, keywords are displayed using circles, while different colors indicate keyword
clusters, and frequency of occurrence is indicated by the size of the circle. The two primary
clusters are: digital twin and artificial intelligence. It is visible that many articles mention
digital twin in the context of artificial intelligence, metaverse and machine learning. There
is a strong connection between digital twin and related concepts such as the internet of
things, Industry 4.0 and blockchain, although it is worth noting that terms like security and
cybersecurity appears alongside these terms. This confirms the previous analysis, which
found that security concerns are common when dealing with digital twin topics.
5. DISCUSSION
Our systematic review of the literature revealed four main privacy and security challenges
associated with digital twins. It served as a follow up to previous literature reviews [Yao et
al., 2023; Lei et al., 2023; Asad et al., 2023]. As stated, this emerging technology needs to
address challenges around the entire digital twin life cycle and their integration into current
frameworks. One of the key challenges were security risks, most frequently mentioned in
the total number of examined articles. They were mostly related to cybersecurity, such as
system vulnerabilities and network communication problems, which can lead to data

breaches and cyberattacks. Previous research confirms that data breaches have become a
significant challenge for organizations [Seh at. Al, 2020; Wheatley et al., 2016], as they
compromise the confidentiality, integrity, and availability of data, known as the security
triad [Umran et al., 2022]. The majority of identified security triad issues pertain to data
confidentiality and integrity, while data availability was less concerning. This category also
encompassed issues related to security authentication, such as identity management, access
control, and unauthorized access. A multi-user system could be a solution for protecting
data from clouds by allowing owners to control access to specific data subsets [Hörandner
and Prünster, 2021]. Authentication mechanisms could help maintain confidentiality in
digital communication, including medical records and other operations [Qian et al., 2022].
The second category of challenges falls under the privacy utilization category, which
includes data processing, data usage, data sensitivity and data-based discrimination. Data
usage referred to general use of personal data, while data processing included the collection,
storage, and data sharing. The paper highlights that most cyberattacks are linked to the
process of collecting and handling large amounts of data [Bruynseels et al., 2018]. For this
reason, Tao et al. [2019] suggested implementation of security and privacy tools that can
achieve overall data protection. Data sensitivity included issues around control over
sensitive data [Hörandner and Prünster, 2021], collection and dissemination [Qian et al.,
2022], as well as confidentiality protection [Araújo et al., 2022]. It was observed various
categories that need to be protected like sensitive project and asset data [Omrany et al.,
2023], manufacturing data [Timperi et al., 2023], critical physical objects and systems
information [Hemdan et al., 2023] and confidential patient data [Turab and Jamil, 2023].
Data-based discrimination is revealed as one of the challenges, explained as people's
tendency to identify patterns in data can lead to prejudice [Bruynseels et al, 2018]. For
example, it can cause patients to be diagnosed as ineligible for surgery or insurance [Popa
et al., 2021]. It can widen socio-economic gaps by not being accessible in countries with
lack of access to research facilities, leading to inequality and injustice [Popa et al., 2021;
Winter and Chico, 2023]. The issue of uneven access is in previous research recognized as
a significant obstacle that hinders the participation of stakeholders [Lei et al., 2023].
The third regulatory compliance category included: (1) data ownership, (2) governance
(3) regulatory frameworks (4) intellectual property, and (5) standardization challenges. Our
review revealed the most problems with data ownership and governance arise from poor
regulatory frameworks and a lack of standards. As indicated by Kwon and Johnson [2013],
fear of potential data breaches motivates organizations to comply with regulatory
requirements. It is important to achieve regulatory compliance with data privacy guidelines
[Cali et al., 2023]. As digital twin development includes frameworks related to specific
industries, devices and artificial intelligence, compliance with each of these regulations
should be achieved [Cellina et al., 2023].
Finally, the fourth category of privacy risks received little attention. It dealt with privacy
preservation and data anonymity, while mentioning various forms of privacy violations such
as personal information attacks, privacy breaches, data leaks, and misuses of private data.
As wireless data transfer may contain content that can jeopardize owners' privacy, it requires
the creation of secure data sharing channel [Son et al., 2022]. Private data must also be
protected, as vehicle DT data, including position and transmission conditions, is vulnerable
to attack when transmitted to the cloud [Yang et al., 2022]. Detailed product information
can facilitate production management, but at the same time it makes it easier for attackers
to learn confidential business know-how [Holmes et al., 2021].

6. CONCLUSION
This study tackled the current state of DT implementation and the challenges that industries
are facing, with a particular emphasis on privacy and security. Findings showed that DT are
implemented in various domains such as construction, infrastructure, manufacturing and
healthcare, which was related to the first research question (RQ1). In response to the second
research question (RQ2), this study identified 4 main categories of privacy and security
challenges: (1) privacy utilization, (2) regulatory compliance, (3) privacy risks, and (4)
security risks. The key technical challenges were cybersecurity and attacks, as well as
authentication problems caused by system vulnerabilities.
Security risks are identified as one of the major challenges that prevents successful DT
implementation. Other reasons included some non-technical aspects that were primarily
related to privacy challenges, such as the use of private and sensitive data, legislation, and
fair data distribution. The results indicate that establishment of an appropriate security
infrastructure could solve a number of non-technical challenges and reduce the possibility
of privacy and security risks. Furthermore, recommendations for further research should be
aimed at creating a common regulatory environment for the development of a digital twin
technology. In particular, the challenges identified in this study can be used as variables or
constructs that can be addressed in developing a digital twin framework. One of the
limitations of this research is that focus was only on privacy and security challenges, while
there are a number of other challenges that prevent its successful implementation. Some of
the open questions relate to what challenges arise in certain phases of the digital twin's life
cycle, so that they can be focused on during each development phase.
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