=Paper=
{{Paper
|id=Vol-3731/paper38
|storemode=property
|title=Exploiting Data Spaces to Enable Privacy Preserving Data Exchange in the Energy Supply Chain
|pdfUrl=https://ceur-ws.org/Vol-3731/paper38.pdf
|volume=Vol-3731
|authors=Luigi Coppolino,Alessandro De Crecchio,Roberto Nardone,Alfredo Petruolo,Luigi Romano,Federica Uccello
|dblpUrl=https://dblp.org/rec/conf/itasec/CoppolinoCNPRU24
}}
==Exploiting Data Spaces to Enable Privacy Preserving Data Exchange in the Energy Supply Chain==
https://ceur-ws.org/Vol-3731/paper38.pdf
Exploiting Data Spaces to Enable Privacy Preserving
Data Exchange in the Energy Supply Chain
Luigi Coppolino1,∗ , Alessandro De Crecchio2,∗ , Roberto Nardone2,∗ ,
Alfredo Petruolo1,∗ , Luigi Romano1,∗ and Federica Uccello1
1
University of Naples ‘Parthenope’, Centro Direzionale, 80143 Naples, Italy
2
Scuola IMT Alti Studi Lucca, Piazza S. Ponziano, Lucca
Abstract
In the modern digital landscape, ensuring secure data sharing within complex infrastructures is not
trivial. This paper presents an in-depth analysis of dataspaces and their crucial role in enhancing privacy-
preserving data exchange within the energy supply chain. The main contribution of the work includes
the design of the architecture of dataspaces, emphasizing their utility in addressing legal and technical
challenges while ensuring data sovereignty and stakeholder trust. Through a focused case study and
STRIDE analysis, the practical application and security benefits of dataspaces are illustrated, underscoring
their significance in fostering a secure, efficient, and collaborative data-sharing environment.
Keywords
Smart Grid, Cybersecurity, Cyber Attack, FIWARE
1. Introduction
In today’s interconnected world, existing systems are asked to fulfil increasing data-sharing
requirements. The rapid expansion of data-centric applications underscoring the real value
of data [1, 2] also affected smart grids and the energy supply chains. With a specific focus
on energy exchange, current data exchange methods exhibit limitations, particularly when
multiple stakeholders, including Transmission System Operators (TSOs) and Distribution System
Operators (DSOs), need to collaborate and share sensitive information. The evolution introduced
by the ‘Common European Dataspaces’ represents a strategic response to these challenges,
fostering an environment where data can be exchanged securely and efficiently while respecting
privacy and data sovereignty [3, 4].
While the importance of a unified data-sharing ecosystem is evident, the energy sector faces
specific privacy and security challenges that need to be addressed. As an example, the integra-
tion and exchange of data among energy stakeholders necessitates robust privacy-preserving
mechanisms to prevent unauthorized access and to ensure the integrity and confidentiality of
the exchanged information. This is crucial for maintaining operational security and trust within
ITASEC 2024: The Italian Conference on CyberSecurity
∗
Corresponding author.
Envelope-Open luigi.coppolino@uniparthenope.it (L. Coppolino); alessandro.decrecchio@imtlucca.it (A. De Crecchio);
roberto.nardone@uniparthenope.it (R. Nardone); alfredo.petruolo001@studenti.uniparthenope.it (A. Petruolo);
luigi.romano@uniparthenope.it (L. Romano); federica.uccello@assegnista.uniparthenope.it (F. Uccello)
Orcid 0000-0002-2079-8713 (L. Coppolino); 0009-0003-6257-9732 (A. De Crecchio); 0000-0003-4938-9216 (R. Nardone);
0009-0003-2970-5864 (A. Petruolo); 0000-0003-2571-8572 (L. Romano); 0000-0001-9243-7047 (F. Uccello)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
and across the energy supply chain [5]. Hence, prioritizing privacy-preserving data exchange
and ensuring data sovereignty are basic requirements guiding this transformation [6].
The remainder of this paper is structured as follows. Section 2 addresses the related work.
Section 3 offers a comprehensive overview to clarify the concept of dataspace and to address
the ambiguities and uncertainties identified. Section 4 outlines specific European initiatives
promoting the advancement of dataspaces, while Section 5 concentrates on efforts pertinent to
the development and enhancement of dataspaces supporting the energy supply chain. Section
6 explores a practical case study where the dataspace paradigm has been applied, providing an
analysis of security considerations and employing STRIDE methodology to assess communica-
tions based on dataspaces. Lastly, Section 7 ends the paper drawing final remarks.
2. Related Work
The challenges linked to privacy-preserving and secure data sharing have been subject to
multiple research works. Among others, blockchain-based solutions have been proposed in
several application domains [7, 8, 9, 10], exploiting the inherent properties of such technology
to ensure traceability and integrity. Some others consider security and privacy, mainly using
encryption algorithms. The work in [11] proposes a framework for sharing encrypted data
in the cloud, addressing concerns about privacy breaches. A different approach is in [12],
where two data-sharing algorithms are designed and evaluated in the context of Industrial IoT.
Different frameworks with a specific focus on data sharing and standardized data formats have
been proposed in different works, as [13, 14, 15, 16]. These frameworks adopt centralized data
management, where data are acquired and then processed more than shared.
The present research is motivated by the specific need for privacy-preserving data exchange
in the energy supply chain, aiming to clarify and tailor the concept of data spaces to this context.
Additionally, advanced privacy-preserving techniques suited for the energy sector’s demand
are identified and deployed. Furthermore, a detailed case study is presented, applying STRIDE
analysis to develop a targeted threat model for data exchange among energy stakeholders. The
scope of the model is to demonstrate how dataspaces can enhance data security and privacy in
the energy supply chain, aligning with European data strategy goals and fortifying resilience
against cyber threats.
3. Data Spaces and Data Sovereignty in Europe
To the best of the authors’ understanding, identifying a universally acknowledged definition of
“dataspace” appears unfeasible. The GAIA-X initiative provides an initial perspective, describ-
ing dataspaces as federated, open infrastructures that facilitate data sharing and sovereignty,
based on unified policies, rules, and standards [17]. Similarly, Open DEI defines dataspaces as
decentralized infrastructures that ensure trustworthy data sharing and exchange within data
ecosystems, adhering to mutually accepted principles [18]. Meanwhile, the European Commis-
sion envisions a dataspace as a unified global marketplace for both personal and non-personal
data, including sensitive business information, and ensuring robust safeguards while providing
businesses with easy access to top-quality industrial data to drive growth and innovation[19].
Clearing
Services
House
TOR
TOR
App Store
Policies Policies
NEC
NEC
Data Marketplace
CON
CON
Data Participants
Data Broker
Description Certificates
Company A Identity Provider (CA) Company B
Vocabulary Provider
...
Company A Cloud Platform Traditional Inter Data Exchaning Company B Cloud Platform
Figure 1: Dataspace-Based Data Exchaning
Given these different definitions and the consequent complexity of such a concept, this
section aims to offer a concise yet right-to-the-point description of a dataspace, detailing the
essential requirements and elucidating the key roles of privacy-preserving mechanisms and
data sovereignty in its definition.
What a Dataspace Is. A dataspace is an open ecosystem facilitating data sharing and
accessibility among different entities. This shift towards dataspaces relies on principles of
transparency and trust that govern these ecosystems. Traditional data exchange methods
face numerous challenges: a primary concern dataspaces seek to mitigate is the complexities
stakeholders experience when establishing legal frameworks for collaboration. Stakeholders
across vital sectors, like energy, encounter obstacles in creating contracts and establishing
reliable data-sharing policies, hindering broader participation and innovation due to legal
complexities. Conventional data exchange lacks full control over post-exchange data, posing
risks in scenarios where data governance is crucial, potentially resulting in financial losses.
What a Dataspace Requires. To establish a collaborative and open environment for data
sharing, developing a trusted ecosystem where each participant is recognized, has assigned roles,
and is authorized for dataspace activities, is essential. Techniques ensuring privacy-preserving
data exchange, authentication, authorization, and overall security are vital, as highlighted in
the European Claim for dataspaces [20]. For this reason, dataspaces are designed to ensure data
sovereignty, providing a framework where data owners maintain control over the usage and
sharing of their data. This is a key concept in establishing trust and encouraging more entities
to participate in data sharing, as it ensures the respect of their data rights and privacy. In a
dataspace, ensuring compliance with regulations and ethical standards is vital, alongside an
interoperable infrastructure supporting data sharing across diverse systems, achieved through
common standards, vocabulary, and protocols.
Privacy-Preserving Data Exchange. In the dataspace paradigm, data exchange is enabled
through connectors, which can be deployed on-premises or in a cloud environment, primarily
using helm charts and Kubernetes clusters, following an architecture ensuring security mech-
anisms. As depicted in Figure 1, establishing the data marketplace requires, among others,
a Certification Authority (CA) and a shared vocabulary. The former ensures that different
stakeholders can be identified and authorized to participate in the marketplace, while the latter
enables participants to comprehend a shared language. This common understanding is essential
for facilitating machine-to-machine (M2M) communication and simplifying the creation of
privacy-preserving policies. From a technical perspective, it is also essential to guarantee the
minimum requirements needed for stakeholders’ computing nodes, verify the level of security
provided (e.g., Trusted Execution Environment support [21]), and confirm their geographical
locations. Stakeholders can publish descriptions of their data offerings on a data broker, while
developers can provide applications that utilize this data to create added-value services. All
transactions between different connectors are recorded by a clearing house, to ensure the accu-
rate processing of payments and data exchanges. Examples of policy enforcement in dataspaces
include restrictions on the duration of data usage, the rights to view and utilize data, and the
conditions under which data may be shared or processed. For instance, policies might dictate
that certain data can only be accessed for a limited time, or specify that data must not be
transferred to unauthorized parties. Additionally, policies can enforce data anonymization or
de-identification before it is shared to protect privacy. These rules ensure that all data handling
within the dataspace adheres to agreed-upon ethical and legal standards, fostering a secure and
trusted environment for all participants.
4. The European Initiatives for Data Spaces
The evolving data-driven landscape and the need for value-added services drive European efforts
to establish Common European data spaces [22]. This section highlights the key stakeholders
shaping this shift to a novel paradigm, prioritizing European values and facilitating new criteria
for data sharing.
4.1. International Data Space Association
The International Data Space Association [23] (IDSA) plays a significant role in the data-sharing
revolution. Its mission focuses on ensuring data sovereignty and bridging the gap between
industry and research communities to establish data spaces. From a technical point of view,
the association has devised the IDSA RAM [24], a reference architecture that delineates the
technical and organizational principles for implementing dataspaces. The Reference Model
(Figure 2) encompasses three primary aspects: security, certificates, and governance, organized
into five layers of granularity: Business, Functional, Information, Process, and System. The
Business Layer is devoted to articulating business models and value chains, clarifying the roles
and interactions of stakeholders to ensure alignment with business goals. In the Functional
Layer, the key capabilities and services necessary for data exchange are delineated, including
specific functions such as data sharing and processing that underpin business requirements.
The focus of the Information Layer is on organizing data, defining its semantics, and managing
its governance to guarantee that data can be exchanged and interpreted across diverse systems.
The Process Layer concentrates on the operational aspects, defining the processes and protocols
that ensure data is exchanged securely and efficiently, adhering to established policies and
standards. Lastly, the System Layer is concerned with the technical foundation, specifying the
necessary infrastructure and components, like connectors and networks, that enable the secure
exchange of data within the data space. IDSA’s key contribution includes the specification of
BUSINESS LAYER
Roles Interactions
FUNCTIONAL LAYER INFORMATION LAYER
DOMAIN
TRUST SECURITY MODEL SPEC
VOCABULARY
DIGITAL RESOURCE
DATA ECOSYSTEM INTEROPERABILITY
REPRESENTATION
APPS MARKET
PROCESS LAYER
ONBOARDING DATA OFFERING
SYSTEM LAYER
CONTRACT
SERVICE DATA EXCHANING
DATA NEGOTIATION
ARCHITECTURE
DATA APPS
Figure 2: IDSA RAM Architecture Specification.
Connectors, essential for establishing a dataspace instance. These, detailed in the IDSA RAM,
enable multiple parties to create the data-sharing platform.
4.2. FIWARE
As data-driven technologies are becoming increasingly integral in today’s digital world [25], the
FIWARE foundation plays a crucial role in supporting the development of smart applications
and services [26, 27]. FIWARE provides open-source software enabling developers to craft
data-centric applications. The building blocks are the Generic Enablers, which offer a range of
functionalities to integrate various data sources and services. This enhanced ecosystem of open-
source resources is crafted to optimize the development process, simplifying and accelerating
the ability of developers to create advanced, data-centric solutions. FIWARE is crucial in the
realization of Digital Twins: the core is the Context Broker, a Generic Enabler that orchestrates
the management and storage of context information, bridging the physical devices and their
digital replicas. The Context Broker acts as a central hub, ensuring consistent, up-to-date context
information between various data sources and applications. It synchronizes real-world entities
with digital twin models, enabling real-time responsiveness and dynamic updates. FIWARE’s
commitment to interoperability and manufacturer-independent solutions is evident in its IoT
agents, which translate messages from diverse protocols into a standardized format, facilitating
the creation of manufacturer-agnostic solutions within the FIWARE ecosystem.
This approach is fundamental in advancing the concept of data spaces, where diverse data
sources and systems coalesce in a unified, interoperable environment. Additionally, FIWARE’s
contribution extends to the standardization of data models through its Smart Data Models
initiative. This effort provides a common vocabulary and a set of standardized data structures,
vital for the homogenization of data and the facilitation of interoperable data exchange. These
data models offer significant value in the context of data spaces, enabling disparate systems to
understand and interpret shared data consistently, and enhancing collaboration and innovation
in data-driven environments.
5. The Energy Data Space and the CIM
The European Common Data Spaces in the energy sector are crucial for several reasons as
illustrated in Table 1. It offers key benefits and enables utilities and governments to develop
new services for citizens and uncover new revenue streams. It is a transformative paradigm
in the energy sector, uniting stakeholders—energy providers, consumers, grid operators, and
regulators—under a shared digital context. This unification is not only about connecting dots
but about creating a secure exchange of data that bridges the traditional silos, fostering a
seamless flow of information and insights across the energy landscape. Such integration and
interoperability are fundamental for the sector’s efficient resource management and distribution,
ensuring that energy reaches where it is needed most when it is needed. Within this context,
initiatives like Enershare [28] and CyberSEAS [29] emerge in the realization of this vision.
Enershare’s objectives are emblematic of the broader ambitions of the European Common Data
Spaces. It aims to democratize energy data, making it accessible and actionable for a spectrum of
stakeholders. This democratization is not only about data sharing but about creating a platform
where this data can be transformed into actionable intelligence, driving decision-making, and
innovation across the energy sector. In Particular, Enershare focuses on harnessing the power of
shared data to enable more sustainable energy practices, enhance grid efficiency, and foster the
development of new business models and services that can contribute to the energy transition.
Indeed, the CyberSEAS Project, funded by the EU, targets the crucial objective of securing
European data spaces among its strategic objectives. This initiative underscores the importance
of robust security measures in the realm of data sharing and exchange. CyberSEAS is dedicated
to advancing applications of enabling technologies that are crucial for facilitating privacy-
preserving data exchange and sharing among various utility operators. By focusing on these
technologies, the project aims to establish a secure framework that ensures data confidentiality
and integrity across different entities within the utility sector, enhancing trust and collaboration
in European data spaces.
The significance of establishing data exchange mechanisms among various operators and
stakeholders within the energy supply chain is well acknowledged. This aspect has been
addressed in the energy domain through the adoption of standardized schemas for representing
assets. This has been made possible by the existence of an industry standard, the IEC 61970
Series, commonly referred to as the Common Information Model (CIM) [34]. The CIM fosters
interoperability, enabling seamless integration and communication across different systems
and platforms used by utility operators and stakeholders. By adhering to CIM standards,
organizations can achieve consistency in data representation, ensuring that critical information
about assets, operations, and grid conditions is accurately interpreted and shared among relevant
parties. CIM is implemented using RDF (Resource Description Framework). In this context, RDF
is used to define the hierarchical structure of CIM classes and their relationships. Each CIM
class, such as Substation or VoltageLevel, is represented as a resource identified by a unique
Benefit Description
Optimized Energy Distribution Enables more efficient management and distribution of
energy resources by leveraging data on consumption,
production, and grid status [30].
Enhanced Energy Efficiency Facilitates better understanding and management of en-
ergy consumption, promoting energy-saving measures
and technologies [31].
Monitoring Facilitation Supports the integration and effective monitoring of en-
ergy assets within the energy grid [32].
Innovative Services Development Empowers the creation of new, data-driven services for
consumers, enhancing user experience and engagement
[33].
Table 1
Key Benefits of Data Spaces in the Energy Sector
URI (Uniform Resource Identifier). Predicates encapsulate the properties and attributes of CIM
classes, while their respective values are embodied as objects. CIM schemas enable Transmission
System Operators (TSOs) to readily access real-time information on RES generation output,
thereby empowering them to effectively balance supply and demand. Likewise, Distribution
System Operators (DSOs) leverage CIM to acquire data on distributed energy resources (DERs),
which supports grid planning and operation. Therefore, when developing an energy data
space, ensuring services compliant with this schema representation should not be overlooked.
To effectively harness the benefits of CIM while mitigating its drawbacks, the solution is the
development of dataspace services and data applications that are compliant with CIM standards.
6. A Case Study: Cross-Border Energy Data Sharing
This section describes a case study focused on the sharing of energy data, which shows how the
strategic alignment of diverse stakeholder requirements can benefit from a more unified and
effective power grid management. It demonstrates that substantial benefits derive from advanced
data-sharing mechanisms within a well-structured dataspace. This case study comes from the
experience with utility operators, within the CyberSEAS project [29], from multiple national
stakeholders involving a wide array of end-users, integrating them into the dataspace to enhance
collective outcomes. The final objective is to create an environment where stakeholders can
exchange vital infrastructure data, thereby enriching the ecosystem’s value and functionality.
This approach also played a pivotal role in providing essential protections for the public,
mitigating cybersecurity risks that could influence critical services, including billing. The
case study allows us to point out the twofold benefit of the proposed approach: augmenting
operational efficiency and fulfilling the consumers’ privacy.
6.1. Data Flow Diagram and Threat Model
As anticipated in Section 5, to secure the common European energy data space we follow
the STRIDE methodology in conjunction with Data Flow Diagrams (DFDs). The initial phase
Cust
National Stakeholder A
ome
DSO
TSO
r Serv
Serv
Serv
SERVER T-T Communication
ice P
ice P
ice P
rovid
rovid
rovid
SERVER
SERVER
er Tru
er Tru
er Tru
ust Bo undary
TSOs Tr External Data
st
st Bo
st
Source
Boun
Bo u n
unda
dary
Power
dary
OT
ry
Lines
TSOs
PMUs Services SERVER
SERVER
T-S Communication
CIM Data SCADA
IT DSOs
Services Customer
SERVER D-D Communication Services
TSO
WNode
DSO
WNode
dary IT
st Boun Grid Status
s Tru Data
DSO SCADA SERVER D-S Communication
OT
CIM Data
C
National Stakeholder B A_IoT A_DR
LEGEND
Customers C-S Communication
Meter Software
Analysis
IoT Data
DR Physical
Processing Device
Devices
ry
unda
Meter Entity
dary
dary
Measurement
st Bo
Data oun Data
oun
Device Usage Customer
st B
Source
rs Tru
WNode
st B
Data
s Tru
Direct
s Tru
Communication
ome
DSO
Operational Status
TSO
Cust
Data
Figure 3: Energy Stakeholders Data Flow Diagram
applies the STRIDE threat modelling framework and involves the identification of the system’s
boundaries and the construction of the DFD, shown in Figure 3. This diagram identifies different
trust boundaries for each participant in the energy dataspace, acknowledging the premise that
different entities do not inherently trust one another. The diagram reports the various energy
sector stakeholders, such as TSOs and DSOs, abstracting their complex internal communications.
In particular, this DFD shows how data related to the power grid’s operational status and the
CIM infrastructural descriptions are essential for the operational and strategic management
of the grid. These data aspects are crucial for TSOs and DSOs to access real-time information
and for supporting grid planning and operational decisions. Another delineated trust boundary
pertains to customer premises. Data from meters, IoT devices, and Demand Response assets
(DR Devices) can be collected by the utility operator or leveraged to develop new services to
enhance power savings, among other objectives. Beyond direct communication with the utility
provider, there may also be data exchanges with untrusted parties, such as cloud-based IoT
control mechanisms that could be hosted globally. This potential interaction warrants careful
consideration within the threat modelling effort.
Once we have delineated the entire system, we map the assets identified in the DFD with their
corresponding STRIDE threats. This mapping enables us to associate each asset with the specific
security threats it may face, providing a comprehensive understanding of the potential risks
within the system. As depicted in Table 2, each device within this system is subject to various
threats that must be carefully considered. When analyzing the dataspace-facilitated interactions,
it is crucial to recognize that privacy-preserving techniques and policy enforcement measures
must be specifically tailored to address these threats. For example, strong authentication
mechanisms such as multi-factor authentication can be implemented to counteract spoofing.
Policy enforcement can be strengthened by using attribute-based access controls (ABAC) to fine-
tune who has access to data under what conditions. Furthermore, to protect against information
disclosure, data can be encrypted both at rest and in transit, ensuring that sensitive information
remains confidential. These measures, when carefully specified and applied, can significantly
bolster the security of dataspace interactions.
6.2. Dataspace-based solution
While the establishment of dataspace-based communication enables participants to devise their
policies for ensuring privacy during data exchanges, it’s important to highlight the intricate
complexities involved in this solution. Specifically, the adoption of a common vocabulary has
been facilitated through the use of smart data models from FIWARE, particularly those related
to the Common Information Model (CIM). This adoption has streamlined the implementation of
communication, allowing participants to comprehend a shared language and exchange valuable
data effectively for information creation. However, another potential drawback is the reliance
on such standardized models, which may not always accommodate the specific needs or nuances
of all participating entities. While standardization promotes interoperability and simplifies
data sharing, it can also constrain organizations’ flexibility to represent their data in ways that
fully capture its unique aspects or proprietary nuances. Additionally, aligning different data
models to a common standard can be resource-intensive and require significant transformation
or mapping efforts, which could introduce data fidelity concerns. The resulting framework is
depicted in Figure 4, where the interactions among different national stakeholders (including
TSOs and DSOs) and (potential) service providers through the dataspace are mediated by a set
of security policies, well-defined and enforced by the cited technologies and frameworks. It is
important to acknowledge that this set of policies has to be dynamically updated and adjusted
when it is needed.
Figure 4: Dataspace-based solution.
Asset Description STRIDE Element
TSO/DSO Systems Entities managing the transmission Spoofing, Tampering,
and distribution of energy Repudiation
SCADA Systems Systems for monitoring and controlling Information Disclo-
industrial processes sure, Denial of Ser-
vice
Operational Technology Hardware and software that monitors Elevation of Privilege,
and controls physical devices Tampering
Information Technology Systems used for managing and pro- Spoofing, Infor-
cessing information mation Disclosure,
Denial of Service
Smart Meters Devices that measure energy usage and Information Disclo-
communicate this information sure, Denial of Ser-
vice
IoT Devices Devices connected to the internet that Elevation of Privilege,
collect and exchange data Information Disclo-
sure, Denial of Ser-
vice
Demand Response Devices Devices that manage energy usage Denial of Service,
based on demand signals Tampering
Working Nodes (WNode) Nodes responsible for data transmis- Denial of Service,
sion within the network Spoofing, Informa-
tion Disclosure
Common Information Model Standardized models describing electri- Tampering, Informa-
(CIM) Data Sources cal components and configurations tion Disclosure, De-
nial of Service
Grid Status Data Sources Systems providing real-time data on Tampering, Informa-
the status of the electrical grid tion Disclosure
External Data Sources External systems providing additional Spoofing, Informa-
data, like weather services tion Disclosure
Concentrators Analysis Analysis of the data collected from Spoofing, Tampering,
smart meters Information Disclo-
sure
IoT Aggregator Analysis Systems that compile and manage data Spoofing, Elevation
from multiple IoT devices of Privilege, Informa-
tion Disclosure
Demand Response Aggregator Systems that manage and coordinate Spoofing, Tampering,
Analysis demand response signals and data Denial of Service
Table 2
Assets and Corresponding STRIDE Elements in the Energy Dataspace
Example of Policy Enforcement Loop for Securing Connectors. A dynamic approach to
enforce security policies in dataspace connectors begins with the initialization of the connector
using baseline security policies and the importation of an initial STRIDE-based threat model.
The enforcement process is structured as a continuous loop that only concludes when the
connector is deactivated. Within this loop, the connector first collects real-time data on its
interactions. Simultaneously, it integrates both external threat intelligence and insights from
internal monitoring logs to comprehensively update the threat model. As the loop continues,
for each type of interaction that the connector facilitates, it is important to assess whether
the existing security policies adequately mitigate the identified risks. This approach involves
strengthening authentication protocols, refining access controls, and bolstering data encryption
to counteract the assessed threats. Finally after implementing the necessary adjustments, these
updated policies are enforced in real time. This proactive and adaptive approach ensures the
connectors are secured against evolving threats throughout their operational life.
7. Summary and Conclusions
This paper focuses on the pressing need for methods of exchanging data that preserve privacy
and has shed light on the emerging paradigm of dataspaces. These innovative ecosystems allow
for the free flow of data among various parties, facilitating the establishment of domain-specific
data markets that offer novel solutions for stakeholders in industry, research, and institutions.
Among the primary contributions, the work addressed the definition, requirements, and privacy-
preserving techniques of the dataspace. Then, the most significant European initiatives focusing
on dataspaces utilization and contribution to security were presented. Finally, a real-world
case study was shown, to demonstrate the practical applications, the potential benefits, and
the challenges associated with the adoption of this paradigm. The present research highlights
the significant opportunities offered by data sovereignty in improving data exchange across
various sectors. However, realizing its full potential requires a thorough understanding of
its operational dynamics and security measures, to ensure stakeholders fully comprehend its
benefits. By promoting understanding and facilitating the adoption of this paradigm, steps
forward can be made toward secure and efficient data exchange, with a focus on individual and
organizational data rights.
Acknowledgments
This research has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 101020560 CyberSEAS. The content of this
publication reflects the opinion of its authors and does not, in any way, represent the opinions
of the European Union. The European Commission is not responsible for any use that may be
made of the information that this publication contains.
This work has been also partially funded by the European Union under NextGenerationEU
PRIN 2022 Prot. n. 202297YF75 S2: Safe and Secure Industrial Internet of Things. Views and
opinions expressed are however those of the author(s) only and do not necessarily reflect those
of the European Union or European Commission. Neither the European Union nor the granting
authority can be held responsible for them.
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