=Paper=
{{Paper
|id=Vol-3214/WS6Paper2
|storemode=property
|title=Harmonization Profiles for Trusted Data Sharing Between Data Spaces: Striking the Balance between Functionality and Complexity
|pdfUrl=https://ceur-ws.org/Vol-3214/WS6Paper2.pdf
|volume=Vol-3214
|authors=Arjan J.R. Stoter,Bauke Rietveld,Vincent Jansen,Harrie J.M. Bastiaansen
|dblpUrl=https://dblp.org/rec/conf/iesa/StoterRJB22
}}
==Harmonization Profiles for Trusted Data Sharing Between Data Spaces: Striking the Balance between Functionality and Complexity==
https://ceur-ws.org/Vol-3214/WS6Paper2.pdf
Harmonization Profiles for Trusted Data Sharing Between Data
Spaces: Striking the Balance between Functionality and
Complexity
Arjan J.R. Stoter1, Bauke Rietveld2, Vincent Jansen2 and Harrie J.M. Bastiaansen1
1
TNO, Eemsgolaan 3, Groningen, 9727 DW, The Netherlands
2
Innopay, WTC, F-Tower, Strawinskylaan 381, Amsterdam, 1077 XX, The Netherlands
Abstract
The ambition of the EU Data Strategy can be summarized as a ‘federation of interoperable
data spaces’. Currently, a multitude of architectures, frameworks and protocols is used by
various data spaces. The Data Sharing Coalition has provided an architecture framework for
interoperability between data spaces, making use of a harmonization domain and data space
proxies as key architecture concepts. Complete interoperability between a wide variety of
data spaces presents a challenge for the harmonization domain. To enable interoperability
between a variety of data spaces, a set of harmonization profiles are required in the
harmonization domain to provide the necessary functionalities. However, implementing each
harmonization profile comes with additional complexity. Therefore, it is desirable to identify
a minimal set of harmonization profiles to provide interoperability between an adequate
variety of data spaces. This paper addresses the identification of harmonization profiles,
presents a framework for structuring harmonization profiles and explores the impact of key
trust aspects (policy management and trust ecosystem) on harmonization profiles.
Keywords 1
European data strategy, data space interoperability, federation, harmonization profile, proxy
model, access and usage policies, trust ecosystem, IDS.
1. Introduction
Data and data sharing are clearly on the radar of the European Commission. The release of the
European Data Strategy [1], the Data Governance Act [2] and the additional input sought on data
spaces through OPEN DEI [3] illustrate the importance the EU attributes to data sharing for our
society and economy. The goal of the EU Data Strategy can be summarized as a ‘federation of
interoperable data spaces’, for which interoperability of data sharing is vital, both within and across
data spaces. In practice however, there is no single architecture, (legal) framework or protocol stack
that is used by all data spaces. Sectors and communities are currently deploying or developing data
spaces using a variety of approaches [4-6], posing a major challenge for interoperability between data
spaces. An overarching framework to address interoperability is provided by the new European
Interoperability Framework (EIF) [7], distinguishing the levels of technical, semantic, organizational,
and legal interoperability. Establishing trust is a key component within the interoperability framework
and the focus of this paper.
The Data Sharing Coalition (DSC) addresses inter data space interoperability in its Data Sharing
Canvas [6], which describes an architecture framework to enable the trust required for interoperability
between data spaces. The Data Sharing Canvas introduces the concept of ‘harmonization’, which is
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: arjan.stoter@tno.nl (A.J.R. Stoter); rietveld.bauke@gmail.com (B. Rietveld); vince.jansen@innopay.com (V. Jansen);
harrie.bastiaansen@tno.nl (H.J.M. Bastiaansen)
ORCID: 0000-0003-1673-4140 (H.J.M. Bastiaansen)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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defined as ‘the establishment of agreements, standards, and requirements between participants to
enable data sharing between them’. The trust required for data sharing is enabled through harmonized,
overarching trust framework agreements, which data spaces should adhere to. Compliance with these
overarching agreements can be achieved via full or partial harmonization [6].
Full harmonization of data spaces means that data spaces (internally) adhere to the same
requirements and principles, thereby enabling inter data space interoperability. Given the impact in
terms of alignment effort and costs, full harmonization is often not feasible in practice. Therefore, the
Data Sharing Canvas introduces partial harmonization through a new component, called a data space
proxy, that absorbs the complexity of harmonization of data spaces. Proxies allow data consumers and
providers within a data space to simply connect to other data spaces via their proxy. Proxies have the
main functionality of translating data space specific transactions to their harmonized equivalents,
thereby facilitating interoperable transactions and creating an understanding of concepts like trust and
security across data spaces.
Figure 1: Data space interoperability roles and architecture, based on partial harmonization.
The harmonization domain (i.e., the domain between proxies, see Figure 1) uses a technical
protocol, defined as a harmonization profile. Ideally, this is a single protocol that supports all required
functionalities to facilitate trust and data sharing capabilities between all types of data spaces. In
practice, given the variety of possible data space architectures, frameworks, and protocols, it is not
feasible that a single harmonization profile can be used as a technical ‘lingua franca’ in the
harmonization domain. More likely, multiple harmonization profiles are required to facilitate the
interoperability for specific types of data spaces.
The current paper focusses on the trust aspects that are to be supported by the harmonization
profiles. More specifically, it addresses the research question: “How to structure and identify a
minimal set of harmonization profiles that enables trust for interoperability between an adequate
variety of data spaces?”. This research will be addressed in an exploratory manner.
The following section describes main trust aspects of data space interoperability and explores their
interdependencies. The subsequent sections address the main distinguishing features within trust
aspects that lead to different harmonization profiles. The findings section considers the assessment of
the defined harmonization profiles and provides initial validation results, after which the final section
describes conclusions and follow-up work.
2. Trust aspects for defining a harmonization profile
As indicated, this paper focusses on the trust aspects for interoperability of data spaces by means
of the partial harmonization approach, with data space proxies and harmonization models as key
architectural concepts. Two main trust aspects need to be addressed: policy management and the trust
ecosystem. Policy management encompasses access- and usage policies Error! Reference source not
found.. Both express business and regulatory policies. Access policies define which participants are
allowed access to data services, whilst usage policies define what participants are allowed to do with
the data. A trust ecosystem ensures that all interactions between participants in a participant chain are
trustworthy, both within- as well as between data spaces. A trust ecosystem is a prerequisite for data
sharing transactions of (sensitive) primary data.
As a working hypothesis for the remainder of this paper, it is assumed that aspects of policy
management and the trust ecosystem are independent. This implies that they may be developed
independently, and their impact on the number of required harmonization profiles is additive rather
than multiplicative. The latter would -theoretically- reduce the required number of harmonization
profiles drastically. This assumption is further addressed in the findings section. The following
sections address the main distinguishing features of usage policy management and the trust
ecosystem, respectively.
3. Policy management
Governance of usage- and access policies encompasses interactions between a data provider and
data consumer for defining- and agreeing upon policies, as well as the capabilities to enforce them.
Two commonly used approaches are identified here, each with its own main associated
implementation technology, namely policy management with access tokens and policy management
with contract negotiations.
In policy management with access tokens, a two-stage approach is followed in which (1) an access
token is obtained from the data provider, based on approval provided by the entitled party, with which
(2) the data can be retrieved from the data provider. Policy enforcement capabilities are in this case
only required for the data provider, and only short-living, unidirectional sessions between data
provider and data consumer are needed. The OAuth2.0 protocol [8] is commonly used as an
implementation technology for policy management with access tokens, based on generic web service
calls in the form of APIs using access tokens for authentication of data consumers by the data
provider. It is important to note that access tokens can be used to enable access policies only, which
means that potential usage control needs to be implemented separately.
Policy management with contract negotiation is applicable for data spaces with both access and
usage control policies. As in policy management with access tokens, a two-stage approach is followed
in which (1) a data sharing contract is negotiated between a data provider and a data consumer, based
on which (2) the data provider shares the data with the consumer. Policy enforcement capabilities to
support usage policies are required both at the data provider and consumer requiring long-living, bi-
directional sessions between them.
An implementation technology that supports contract negotiation and policy enforcement to
manage usage policies is currently provided by the International Data Spaces (IDS) initiative Error!
Reference source not found. as a secure connectivity protocol, i.e., the IDS Communication Protocol
(IDSCP) Error! Reference source not found.. IDS is attracting major attention as a pillar for the
European Data Strategy [1] and its design principles as being developed by the EU OPEN DEI
initiative [3]. IDS It is being defined by the IDS Association (IDSA) and is standardized Error!
Reference source not found..
Figure 2: Policy management using access tokens (OAuth) (l) and contract negotiation (IDSCP) (r).
Figure 2 depicts the protocol stack for access- and usage policy management using access tokens
and contract negotiation. A distinction is made between protocols for definition-, agreement- and
enforcement of policies. Interoperability between data spaces requires specific harmonization profiles
for both access- and usage policy management that are tailored to the needs and capabilities of each
data space. The two approaches and their corresponding protocols are the foundation for developing
the associated harmonization profiles.
4. Trust ecosystem
An essential functionality of a harmonization profile is to facilitate trust in data sharing between
participants in different data spaces. Compared to data sharing within a data space, facilitating trust
becomes increasingly complex for inter data space interoperability, as it involves a chain of
participants in the various types of roles as depicted in Figure 1. There are core roles (e.g., data
consumers and data providers), intermediary roles (e.g., identity services, authorization services,
brokering services and proxies) and interconnectivity roles. The identity and authenticity must be
established and validated for each participant fulfilling a specific role. Additionally, authorization for
each action of these participants must be verified. Depending on data space implementations, these
mechanisms for identification, authentication, and authorization (IAA) can either be ‘transparent’ or
‘opaque’.
Transparent IAA mechanisms provide a data space with all the required information to do IAA
assessments. In this situation, IAA is the direct responsibility of the participant performing the IAA
(e.g., a service provider). This requires complete (or at least sufficient) transparency of IAA
information across data spaces. A harmonization profile should, in this case, enable communication
and understanding of IAA information across data spaces, that is, to enable information transfer and
mapping between a data space specific format and a harmonized equivalent. Furthermore, the IAA
information must provide sufficient assurance so that it can be regarded as trustworthy, e.g., through
signed claims. It is likely that all forms of transparent IAA could be handled by a single
harmonization profile. Even though functional IAA (that is, how IAA related information is actually
processed) can vary across data spaces, this would not result in fundamental differences in IAA data
transport or translation protocols.
Opaque IAA mechanisms work via delegated decisions, where IAA authorities basically provide a
‘yes’ or ‘no’ answer. Here, IAA decisions can be made on behalf of others, without the need to
explicitly share IAA information across data spaces. Technical trust enforcement measures include
encryption and use of public key infrastructures (PKI) to be able to trust communications and claims.
Due to the high level of international standardization that exists for these technical security measures
(such as TLS/SSL and X.509 certificates), these can be sufficiently covered with a single
harmonization profile.
Based on the above assessment, no wide variety of harmonization profiles seem to be required to
enable trust in the ecosystem and support both transparent and opaque IAA. In fact, a single
harmonization profile for each seems to be sufficient to enable all possible needs and capabilities for
trust across a variety of data spaces.
5. Findings and status
The Data Sharing Coalition has defined an initial harmonization profile in its Use Case
Implementation Guide (UCIG) Error! Reference source not found.. Based on this UCIG, a
representative proof-of-concept (PoC) was realized for controlled sharing of privacy-sensitive
geriatric data between a hospital and a municipality Error! Reference source not found.. For the
trust aspect on policy management and the trust ecosystem, the UCIG and the PoC implement the
policy management with access tokens approach and the opaque approach, respectively. Based on the
results of this implementation, three validation perspectives are addressed below.
Validation of independence of trust aspects. Previously, the hypothesis was made that the trust
aspects of policy management and the trust ecosystem were independent, resulting in a limited
number of harmonization profiles. In the UCIG these elements were independently described, and the
PoC implementation gave a practical confirmation of this independence. Further validation of the
independence of the remaining policy management- and trust ecosystem aspects is to be provided by
representative data space interoperability cases that include trust aspects for the contract negotiation
approach and the transparent IAA mechanisms.
Validation of adequateness of individual harmonization profiles. The functional adequateness of
harmonization profiles can be validated by means of functioning implementations. The UCIG and its
initial PoC explored the functional adequateness of the policy management with access tokens and
transparent IAA mechanisms. The PoC resulted in a fully functioning harmonization profile and data
sharing across data spaces, through which adequacy was confirmed. Findings in the implementation
of the PoC have led to minor updates of the UCIG. Moving forward, additional illustrative and
representative data space interoperability scenarios need to be defined and validated. A harmonization
profile for the policy management with contract negotiation approach between data spaces will be
developed as a next step, resulting in an implementation guide and associated PoC as upcoming for
interoperability developments Error! Reference source not found..
Validation of completeness of set of harmonization profiles. Policy management and trust
ecosystem were identified as the two most relevant functional aspects of the harmonization domain
which could require a variety of harmonization profiles. Initial results suggests that other aspects
(such as digital identities and metadata) do not require multiple harmonization profiles to cover the
variety of required functionalities for alternative implementations. Within both aspects covered in this
paper, two main distinguishing cases leading to individual harmonization profiles have been
described. Market research [4] and experience (i.e. professional judgement of the authors) in the data
sharing context conclude that interoperability between current data space developments are covered
by this initial set of harmonization profiles. Whether additional harmonization profiles will be
required will become apparent in the future developments and projects.
6. Conclusions and future work
A primary objective of this paper was to provide a framework for - and initial exploration and
structuring of - harmonization profiles to support interoperable data sharing between a variety of data
spaces. It was identified that a number of different harmonization profiles were required to enable all
required functionalities for data sharing between a variety of data spaces. The preliminary conclusion
is that data space interoperability may be realized by a limited set of harmonization profiles, for which
two trust aspects (policy management and trust ecosystem) can be independently developed as part of
the harmonization profiles. For both trust aspects there is a limited number of varying protocols that
act as a distinguishing factor for harmonization profiles, of which the two main ones have been
identified in this paper. Independence of both trust aspects suggests that the overall number of
harmonization profiles to be developed in the future remains limited and manageable.
Future work includes (i.) the identification of the (need for) additional harmonization profiles and
the specification thereof, (ii.) developing the additional data spaces interconnect roles and architecture
as depicted in Figure 1, (iii.) elaborate the data space interoperability architecture at other
interoperability levels of the EIF [7], especially at the semantic and legal level, and (iv.) assess the
scalability (with respect to the number of data spaces) for implementations of the identified
harmonization profiles.
7. Acknowledgements
The work as presented in this paper extends upon the architectural foundation for interoperability
between data spaces as laid by the Data Sharing Coalition. It has been supported and co-financed by
the Data Sharing Working Group of the Dutch AI Coalition (NL AIC, https://nlaic.com/en/building-
blocks/data-sharing/). In addition, this paper builds upon the work done within the Dutch Research
project DASLOGIS, supported by the Dutch Top consortia for Knowledge and Innovation Institute
for Advanced Logistics (TKI Dinalog, www.dinalog.nl) of the Ministry of Economy and
Environment.
8. References
[1] European Commission, A European strategy for data, 2020. URL:
https://ec.europa.eu/commission/presscorner/detail/en/ip_20_273, https://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:52020DC0066&from=EN.
[2] European Commission, Proposal for a Regulation of the European Parliament and of the Council
on European Data Governance (Data Governance Act), 2020. URL: https://ec.europa.eu/digital-
single-market/en/news/proposal-regulation-european-data-governance-data-governance-act,
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0767.
[3] OPENDEI, Design Principles for Data Spaces – Position Paper, 2021. URL: https://design-
principles-for-data-spaces.org/.
[4] The Netherlands AI Coalition, AI Ecosystem & Market Analysis: Quick scan of data sharing
market to validate blueprint of the NL AIC, 2020. URL: https://nlaic.com/wp-
content/uploads/2021/02/AI_Ecosystem_and_Market_Analysis_Data_Sharing_4-feb-
2021.pdf.pdf.
[5] TRUSTS, Trusted Secure Data Sharing Space, 2022. URL: https://www.trusts-data.eu/.
[6] Data Sharing Coalition, Data Sharing Canvas - A stepping stone towards cross-domain data
sharing at scale, 2021. URL: https://datasharingcoalition.eu/app/uploads/2021/04/data-sharing-
canvas-30-04-2021.pdf.
[7] European Commission, Directorate-General for Informatics, New European Interoperability
Framework (EIF): Promoting seamless services and data flows for European public
administrations, 2017. URL: https://ec.europa.eu/isa2/sites/isa/files/eif_brochure_final.pdf.
[8] IDSA. Data Sovereignty: Updated Position Paper on Data usage Control in the IDS, 2021. URL:
https://internationaldataspaces.org/data-sovereignty-updated-position-paper-on-data-usage-
control-in-the-ids/.
[9] D. Hardt, The OAuth 2.0 Authorization Framework, 2012. URL:
https://datatracker.ietf.org/doc/html/rfc6749.
[10] International Data Spaces Association, Reference Architecture Model. Version 3, 2019. URL:
https://www.internationaldataspaces.org/wp-content/uploads/2019/03/IDS-Reference-
Architecture-Model-3.0.pdf
[11] Data Sharing Coalition, Use Case Implementation Guide, 2022. URL:
https://datasharingcoalition.eu/app/uploads/2022/03/data-sharing-coalition-use-case-
implementation-guide-ucig.pdf
[12] The Netherlands AI Coalition, Dataspace Proxy PoC, 2022. URL:
https://gitlab.com/nlaic/dataspace-proxy-poc
[13] IDSA, TNO Will Set Up Next-Generation Data Spaces with NTT, 2022. URL:
https://internationaldataspaces.org/tno-will-set-up-next-generation-data-spaces-with-ntt/