=Paper=
{{Paper
|id=Vol-3407/paper5
|storemode=property
|title=Towards More Secure and Data Protective Intelligent Infrastructure Systems
|pdfUrl=https://ceur-ws.org/Vol-3407/paper5.pdf
|volume=Vol-3407
|authors=Mariia Bakhtina
|dblpUrl=https://dblp.org/rec/conf/caise/Bakhtina23
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==Towards More Secure and Data Protective Intelligent Infrastructure Systems==
https://ceur-ws.org/Vol-3407/paper5.pdf
Towards More Secure and Data Protective Intelligent
Infrastructure Systems
Mariia Bakhtina1
1
University of Tartu, Narva mnt 18, Tartu, 51009, Estonia
Abstract
With the megatrends of hyperconnectivity, the information systems transform into intelligent infras-
tructure (II) systems that allow data-based decision-making based on data processing. While the real-
world use cases of II systems are just emerging and still are under active research and development, the
problem of methods for information security and privacy protection in such SoSs is even more under-
researched. As a result, there is a gap in the knowledge base in the guidelines on how information
security officers should anticipate required changes to the organisation’s information security strategy
and formulate new plans, in case the organisation transforms its IT system towards the II system. In
this research project, we want to create guidelines on how security risks and privacy protection should
be managed in complex intelligent infrastructure systems. In this paper, we present the context of the
PhD research work, research questions, the methodology and expected contributions of the study.
Keywords
Information security, privacy, identity management, system of systems, intelligent infrastructure
1. Introduction
With the global megatrends of hyperconnectivity [1, 2] and the need for super platforms
(i.e. super apps) of the growing up Gen Z population [3], the System of Systems (SoS) become
more relevant and ubiquitous than ever. In the classical definition, a system of systems refers
to systems which were initially composed independently, but which act jointly toward the
common goal through the synergy between them [4]. Meanwhile, hyperconnectivity means
“the connectivity and interaction of everything that exists in digital environments, including
systems, devices, objects, things, processes, activities, people, and data” [5].
In practice, there are a number of SoSs which enable hyperconnectivity. The implementa-
tions vary from the Internet of Things (IoT) and smart devices used by the end consumers,
followed by Industry 4.0 and by the intelligent infrastructure (II) systems across industries
(e.g., e-government, transportation, healthcare) where organisations’ information systems are
interacting with each other to enable collaboration between stakeholders. Such heterogeneous
systems connectivity enables new data flows, which, in turn, allows data-based decision-making
based. On the other hand, it also opens new attack vectors to the systems and to the security
and privacy of manipulated information [6, 7] because of the non-compositional nature of SoS.
Proceedings of the Doctoral Consortium Papers Presented at the 35th International Conference on Advanced Information
Systems Engineering (CAiSE 2023), June 12–16, 2023, Zaragoza, Spain
" mariia.bakhtina@ut.ee (M. Bakhtina)
0000-0002-0940-9713 (M. Bakhtina)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
35
Thus security of each component in the intelligent infrastructure does not refer to the security
of the whole SoS. As much as each separate system managed by the organisation should have
its security and privacy objectives, additional information security and privacy management
measures of the intelligent system as a whole must be in place. While the traditional security risk
management approaches have been used for ages but mostly applied for the closed, one-purpose
non-dynamic information system, there is a gap in validating their utility and effectiveness in
developing complex SoSs. Currently, the real-world use cases of hyperconnected systems of
systems are just emerging and still are under active research and development, the problem of
methods for information security and privacy protection in such SoSs in the context of high
attention to privacy and trust is under-researched. As a result, there is a gap in the knowledge
base in the guidelines on how information security officers should anticipate required changes
to the organisation’s information security strategy and formulate new plans [8] in case the
organisation transforms its IT system towards II system.
In this paper, we present a research plan to define the knowledge database by investigating
the following research question (RQ): How can security risks and privacy leakage be managed
in intelligent infrastructure systems? Our research considers an intelligent infrastructure system
as a system of systems that may be composed of heterogeneous components and where each
sub-system or component has its own scope of functionality and information processes while
their composition enables delivering new value proposition to end users thanks to the enabled
information flows. By answering this question, the PhD project aims to develop recommen-
dations on managing information security and data privacy in II systems to help information
security specialists in II industries to conduct their key tasks.
The paper is structured as follows. Sec. 2 described the problem areas addressed in the PhD
research. Sec. 3 review the related work. Sec. 4 presents the research questions, the research
approach, and the current state of the research. Finally, Sec. 5 concludes the paper.
2. Problem Statement
This research is composed of three studies that investigate different facets of II SoS and
corresponding problem areas. The first problem area concerns the basic assumptions of the
cooperation between systems in the II system. Specifically, the question of trust between
cooperating stakeholders and systems is one of the fundamental corners stones. Trust is an
assumption based on which systems and organisations decide which systems are trustworthy to
communicate with [9]. Depending on the selected root of trust, systems may rely on different
identity management systems. Thus, for emerging II systems, it is vital to understand which of
the existing trust models is the most appropriate and upon which identity management system
to build the collaboration [10, 11]. Moreover, the properly selected trust model and identity
management system have a direct impact on the II system acceptance, as the trust of end users
in the system is, to a large extent, defined by their awareness and belief that the system delivers
an acceptable level of security and privacy protection for their data [12].
The second problem area concerns assuring the privacy of personal data and protection of
organisations’ sensitive data in the II systems (IISs). The transformation from a few separate
systems which exchange valued data internally towards the exchange of such data between
36
systems inside the IIS tightens connections and blurs systems’ boundaries. The connections
of systems to enable cooperation in IISs involve reliance on collaborative data processing and
management [13]. However, sometimes exchanged data may include (part of) sensitive personal
data or sensitive organisational data. So here comes the question of finding the trade-off between
the benefits of data sharing and the risks of sharing sensitive data or too much data, which can
be reconstructed to reveal some sensitive data (i.e. privacy leakage) [13, 14].
Finally, the third problem area originates in the current maturing of the II systems, the growing
number of implementations and system designs. While the II systems are becoming a reality and
are used across industry domains (including smart parking solutions, e-government services,
e-grids, and smart wearables in e-health) and geography, the development and usage of such
innovative systems reveal new security and privacy management risks and challenges [15, 16].
For example, researchers highlight [17] that when II systems are built based on the existing
disintegrated standalone systems where security measures were separately managed, their
connectivity results in the lack of consistency and coordination in security measures, scattered
around systems users’ identities which causes impediments to the system usability. Moreover,
the new information flows and tighter interaction points between systems most certainly change
the risk profile, and the security and privacy measures which were used before transformation
may become outdated and not effective. To this end, we see the gap in research on how the
transformation of the information security management system (ISMS) progresses in the IISs
and which aspects of security and privacy management are the most challenging.
3. Related Work
3.1. Identity management
Nowadays, intelligent infrastructure systems are developed based on the existing standalone
information systems thanks to their interoperability characteristics or through using data
exchange layers [17, 18]. Therefore, it is natural that the identity management (IdM) system
used in the traditional information system, which is based on the public key infrastructure (PKI),
will be inherited in II SoS. However, centralised PKI is prone to a single point of failure.
Currently, researchers investigate the advantages of self-sovereign identity (SSI), which is an
alternative to the centralised trust model. In [19, 20], the SSI usage is researched primarily by
showing the feasibility of SSI application for system end users and highlighting the advantages
of blockchain for IdM. Other research [10] acknowledge that SSI is an effective way of managing
digital identities and assuring data protection. Our research differs from the existing work by the
context - namely, the identity of a legal entity, which contributes to the existing knowledge body
by investigating how organisations’ identity management differs from end users’ identities.
3.2. Privacy analysis
Data leakage is the release of sensitive data to an untrusted environment. One of the ways
to regulate and push organisations to prevent data leakage is the introduction of privacy
regulations (e.g., GDPR in the EU). Thus, regulations force organisations to comply with their
requirements. In [21], the authors proposed a method for model-driven compliance checks with
37
GDPR. However, they focus on assessing the text of privacy policies only, which may not reflect
the full process during which the data is manipulated. A legal-URN framework is proposed
in [22] for checking the legal compliance of business processes, but it implies developing a goal
model for the European regulation by the tool user from the very beginning. Most of the other
available tools for privacy conduct exclusively the analysis of a single mobile application and
detect data leaks there. The closest to our research is work in [23], where authors propose a
method for privacy analysis in IoT applications by applying a number of assessment methods.
3.3. Current state and challenges in the II system implementations
To develop the recommendations for the developing II industry on measures of information
security and privacy management, the current gap and challenges faced by the companies
in II while developing,managing, and using the II SoSs should be identified. To the best of
our knowledge, there are no existing studies which have approached such gaps at the time of
writing. The closest studies are the following two [24, 25]. In [24], the authors conducted an
SLR of information security and privacy in intelligent railway systems. The authors identified
the state-of-the-art research directions. Additionally, they classified existing challenges and
solutions related to information security and privacy, which are addressed by the research
considering technical, social, regulatory and ethical approaches. Similarly, in [25], the authors
reviewed the knowledge body about security and privacy challenges in smart cities. The study
resulted in the identified security and privacy concerns and threats grouped by the smart city
operation areas and the open challenges in these areas. The study also mapped the existing
protection technologies to smart cities’ security and privacy areas. Our research is similar in
the way and goals of literature sources identification, but in our study, we will have a broader
focus and use the information security frameworks for guiding data extraction and analysis that
additionally consider the organisational and strategical dimensions. In contrast to the related
work, we also aim to identify the current state of regions and compare it with state-of-the-art
research to pinpoint areas where research findings do not find their way to real-life solutions.
3.4. Recommendation on managing information security and data privacy
ENISA [26] in its guidelines for security IoT defined a mapping of good practices that shape
objectives of actors involved in the IoT supply chain regarding the most relevant cyber security
threats for the specific supply chain stage. The guidelines are also supported with a summary of
the most relevant security standards, which should help with defining measures to fulfil good
practices. The current study aims to deliver the analogue contribution but for the actors that
rely on the connected, intelligent infrastructure systems, which are mainly presented by the
connected information systems rather than resource-constrained IoT devices.
4. Research Approach and Contributions
In our research, we address each problem area described beforehand separately, and consoli-
date the separate findings into the final set of recommendations (see Fig. 1).
38
Figure 1: Thesis structure
Each of the three studies has its own research method with evaluation, while the fourth study
aims to validate the aggregated results from the first three studies and design the recommenda-
tion based on the developed before artefacts and findings. The PhD research should result in
an overview of state-of-the-art means (policies, approaches, methods, techniques, and tools)
for II systems protection accompanied by recommendations on how to approach information
protection when transforming your business and IS into an II system. The following section
describes how each research area is approached and reports on the current progress.
4.1. Identity management and SSI - Case of distributed data exchange
systems
This exploratory study aims to investigate the usage of the emerging concept of self-sovereign
identity (SSI) and its effect on security and privacy management. In particular, the research
aims to scrutinise the feasibility of a decentralised PKI approach for organisations’ identity
management (IdM) in data exchange systems.
Method. The research sub-question (RQ1 ) of this study sub-project is as follows: how can the
organisations’ identity be managed using a decentralised approach? We follow the design science
approach [27] to solve the business problem for the X-Road data exchange system. X-Road
serves as the backbone of the Estonian, Icelandic (and a few other countries) digital government
infrastructures. Based on the selected system, we developed artefacts for the transition to the
decentralised IdM approach. To validate the artefacts, we are working on the proof-of-concept,
which will help assess the feasibility and quality of the decentralised approach compared to the
centralised one. This allows us to reveal the advantages and limitations of using decentralised
PKI based on the blockchain for managing organisations’ identities. The first cycle of testing
revealed the need for extending the artefacts with the design on the organisation’s identity
wallet management. Therefore, we also investigate the usage of smart cards and hardware
wallets for managing the keys of organisations’ identities. Thus, we explore how employees’
personal wallets could be used for the delegation of operations on behalf of the organisation.
39
Current progress. So far, we have developed an architecture for the data exchange system
enabled by decentralised PKI processes of managing members [28]. In [29], we propose an
approach of system analysis that should guide organisations that considers the transition to
decentralised identity management. Currently, we are testing the developed artefacts through
the proof-of-concept implementation.
4.2. Privacy leakage analysis
In the second study sub-project, we investigate how to prevent privacy leakages (including
leakage of personally identifiable data as well as the organisation’s confidential data). The study’s
primary goal is to identify and assess the existing state-of-the-art tools which organisations
could use as a part of their tool sets for ensuring sensitive data privacy in a business process
with collaborative data processing.
Method. The research question (RQ2 ) of this part of the PhD study is as follows: how can tools
support the assurance of privacy in a business process? First, we investigate the existing tools
which help to define requirements for privacy assurance of personal data according to GDPR.
Answering this question, we will have a defined procedure for elicitation of requirements to
comply with the GDPR requirements. Second, we determine how tools help to compare the
effectiveness of privacy-enhancing technologies in the business process context. Answering
the second question, we will support the selection of technical measures for privacy assurance
based on their effectiveness in protecting data objects from leakage.
Current State of the Research. So far, this part of the study has resulted in [30]. As the
starting point, we selected the two tools: (i) DPO tool1 for checking the compliance of a business
process with the GDPR requirements, and (ii) Pleak tool2 for detecting possible data leakages.
In [30], we present a method for managing data privacy with respect to GDPR, which relies
on the usage of the selected tools. The method’s usability is validated by the experimental
application of the method to a ride fulfilment process in a ride-hailing company enabled by
an autonomous driving system. In the future, the proposed method will (i) undergo another
round of validation to prove its usability by applying it to another use case of collaborative data
processing, and (ii) be extended by comparing other existing tools for data privacy assurance.
4.3. Current state and challenges of the information security and privacy
management in the II system implementations
The goal of this part of the PhD study is to identify the challenges of information security and
privacy management in the selected domain of II, namely, intelligent transportation. The study
aims to define which security and privacy standards are applicable in the selected industry, how
they are practised, which information security and privacy management methodologies, tools
and technologies they use during the intelligent system lifecycle. As a result, we plan to define
1
DPO Tool can be accessed at https://dpotool.cs.ut.ee/
2
Pleak can be accessed at https://pleak.io/
40
how mature is information security and privacy management in the companies of the selected
region compared to the state-of-the-art solutions.
Method. To achieve our goal and answer RQ3 (What are the challenges in information security
and privacy management in II systems?), we plan to conduct an empirical study of sector analysis
of the intelligent transportation systems in the selected regions. Thus, we will (1) define the
state-of-the-art measures of information security and privacy management; (2) survey the
companies (i.e. sector analysis) to compare their level of information security and privacy
management maturity with the level of the state-of-the-art solutions.
First, the state-of-the-art measures will be defined based on the systematic literature review
(SLR) following the corresponding guidelines [31]. For SLR, we use the following query: ("se-
curity" OR "privacy protection" OR "data protection") AND ("technologies" OR "measure"))
AND (Industry_name), where instead of Industry_name, we use "vehicle sharing", "smart
parking", "tool collection", and "traffic management" and their synonyms. From the selected
papers, we extract policies, approaches, methods and techniques for information security and
privacy management (referred to later as “measures”). The data from the SLR is organised in the
four dimensions (processes, organisational design, people and technological solutions) based on
ISACA’s BMIS [32], McCumber’s Cube [33] and RMIAS [34].
Second, the intelligent transportation sector companies should be surveyed using a question-
naire and interviews to identify their level of information security and privacy management
maturity. The questionnaire and interviews are developed using the identified technologies and
measures. The profile of the information security and risk management in the state-of-the-art
and in each surveyed company should be created to characterise four dimensions.
Current progress. Based on the theoretical model for information security and privacy
management and the results from the sector analysis, we will define the challenges in the
selected industry. The challenges should guide future research directions to support a broad
audience’s development and acceptance of intelligent infrastructure. The identified gaps between
state-of-the-art security and privacy states will be used as information materials for the sector
companies to raise their awareness about the measures to be implemented to improve II systems.
4.4. Recommendation on managing information security and data privacy in
II system
The goal of the final contribution is to help a chief information security officer (CISO) and
cybersecurity architect in organisations using II SoS to conduct their key tasks defined in the
European cybersecurity skills framework [8]. Specifically, we aim to help assess the organi-
sation’s cybersecurity posture when the organisation transforming their IT system towards
intelligent infrastructure system by integration of IoT and with external ISs. As a result, CISO
should have a guide on defining security requirements, goals and checking their alignment with
the organisational objectives.
Method. To answer the research question RQ4 (What is the procedure of transforming the
information security management system when moving towards an intelligent infrastructure sys-
41
tem?), we will analyse the requirements for the security II systems identified in the previous
contributions. Their consolidation should follow a structure similar to ENISA’s guidelines [26]
and map the actors of II system with the recommendations defining cybersecurity goals, re-
quirements strategies and policies, aligned with a business strategy that aims to leverage
hyperconnectivity through intelligent infrastructure systems usage. The recommendations
development will follow the design science methodology [27] which includes the evaluation
and dissemination steps as mandatory in the artefact design and problem addressing.
Current progress. This part of the PhD research study is based on the results from other
research parts. Therefore, the progress in each of the studies discussed above contributes to
scoping the problem and defining the objective of the solution.
5. Concluding Remarks
In this PhD research, we tackle the problem of assuring information security and data protection
in the heterogeneous system of systems used as intelligent infrastructure for producing the
public good for end users. Expected contribution includes revealing how security risks and
privacy protection are managed in the complex intelligent infrastructure systems, what are the
current challenges and how trust between the II sub-systems could be managed to promote the
systems’ acceptance and usability.
6. Acknowledgments
This Ph.D. thesis is supervised by Prof. Raimundas Matulevičius. This research is supported by
the European Union under Grant Agreement No. 101087529. Views and opinions expressed
are however those of the author(s) only and do not necessarily reflect those of the European
Union or European Research Executive Agency. Neither the European Union nor the granting
authority can be held responsible for them.
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