We are happy to introduce you to the newest research on data markets, technology innovation, and the implementation of data sovereignty. In this edition of the newsletter we will take a look at certain key topics such as use cases in finance and telecommunication, data privacy, data markets, legal framework, and functional requirements. To engage you in some interaction, we added some exciting events at the end. Get involved and let´s innovate the European data market together.


With the TRUSTS project, the European Commission initiates a pan-European solution for data-related issues of the future. Aim: innovating European data markets through trust, security, and federation.

Data sharing shapes the economic and societal future of Europe and bears huge potentials, but also comes with risks and apprehensions. The EU project TRUSTS was established to identify and overcome legal, ethical, and technical challenges of cross-border data markets while upholding European values such as privacy and data protection.

17 international partners combine their knowledge and experience in research and business. They are located in Austria, Belgium, Cyprus, Germany, Greece, Israel, the Netherlands, Romania, and Spain.

Learn more


Use cases

To demonstrate and realise the added value of the TRUSTS platform, three business-oriented use cases (UCs) showcase the sharing, trading, (re)use of data and services, and added value generated through innovative applications built on multiple open and proprietary data sources.
The use cases target corporate business data in the financial and telecommunications operator industries.

Use Case 1: The Anti-Money Laundering (AML) compliance use case

The ambition of EBOS, FNET and InBestMe is to classify the business and the technological opportunities that derive from the TRUSTS data marketplace.
Financial institutions, corporate audit depart-ments, tax advisors and many more need to do AML checks. UC1 aims at establishing and validating how data shared via the Platform can feed into an existing AML solution enhanced with big data analytics, for providing faster and more accurate detection of financial crime and money laundering, and how these enriched data can be securely traded via the platform. Artificial Intelligence (AI) and Machine Learning (ML) techniques will be applied and are expected to make a significant and valuable difference in AML.

Use Case 2: The agile marketing through data correlation use case

In this scenario, we establish and validate how big data analytics techniques applied on data shared via the TRUSTS platform can provide timely and meaningful information towards targeting profitable customers at a local level.
The use case will be collecting financial/personal data and telecommunications customers’ data from respective banking and telecommunication enter-prises participating in TRUSTS (Piraeus Bank, FNET). Data will be anonymised by FORTH according to the principles established in the GDPR and company compliance policies ensuring the extrapolation of any private and sensitive information.

Use Case 3: The data acquisition to improve customer support services use case

The TRUSTS Data Marketplace vision is to create an out-of-the-box analytics solution for the anonymisation and visualisation of Big Financial Data, specifically to advance new ways of human-computer interaction currently in their infancy.
The purpose of this demonstrator is the development of an innovative input in the field of debt collections. The idea is that through enhanced analytics, artificial intelligence and the integration of bots, a bank will be able to run a full operation around debt collection without needing to employ agents to follow-up with customers.
Relational Romania in collaboration with the data warehouse of Alpha Bank group, will generate anonymised benchmark datasets using data management procedures that will be set up to transmit all the data.

Author: Gianna Avgousti, eBOS Technologies Ltd, Cyprus



Privacy Preserving Smart Contracts:
Synopsis of a Dell Solution

A blockchain, in its standard configuration, is a structure in which data is available to all participants, in unencrypted form, which presents a problem for applications which require data privacy.
This is because blockchains were not originally intended to be fully private structures, rather they were for integrity checking purposes; logging a verifiable chain of transactions.
Privacy preserving technologies, specifically their application to smart contracts, are an interest of the TRUSTS project smart contracts task.

A more practical solution to privacy issues on a blockchain explored by Dell EMC is the use of hashing algorithms. Each participant stores their private data off-chain using their own infrastructure and places the hash values of said private data on-chain.
Once stored on the blockchain, a given hash value can be used to verify that the generated Hash of a private data artifact matches that which is stored on the blockchain, providing the ability to verify the integrity of data artifacts in a privacy preserving manner.

Any processing of said private data in this solution occurs off-chain, with processing tools, or smart contract programs, being selected via participant-consensus, for example using on-chain voting.
This selected data processing utility will then be greenlighted to push the outcomes of its processing to the blockchain. This allows for non-sensitive inferences about data to be stored on-chain, while protecting the privacy of the underlying source data which was processed.

The addition of Trusted Execution Environments and Secure Enclaves to this solution, within which the selected data processing utilities would run, provides additional robustness; addressing the concern that a dishonest participant may interfere with their instance of the data processing software.
Smart contract software would be run in such a Trusted Execution Environment and then propagate results to the blockchain.

Author: Alan Barnett, Dell EMC, Ireland



Legal regime for data sharing within the TRUSTS project

Data sharing is essential for the functioning of the data marketplace. Whether it takes place between platform partners or with data customers, it generates security, ethics, and privacy issues. Which legal regime applies to data sharing within the TRUSTS project, is not a black-and-white scenario.

As a general rule, the principle of contractual freedom governs contractual arrangements. This freedom, however, may be limited by mandatory legislative provisions. These include the GDPR (when personal data is involved), sector-specific obligations, contract law provisions, intellectual property rights legislation, financial regulations, competition law restrictions as well as national legislation.

In the context of non-personal data being shared on the TRUSTS platform, the Free Flow of Non-Personal Data Regulation does not impose any obligations on businesses, nor does it limit their contractual freedom to decide how their data is to be shared.

Due to the lack of a specific legal regime governing data sharing in business-to-business (B2B) settings it takes place mainly on the basis of contractual agreements (i.e. data sharing agreements).

However, as each dataset is different there is no one size fits all solution for data sharing. Depending on the specific needs of the parties, data sharing may take different forms, such as reciprocal exchange of data, one-off disclosures of data, several partners pooling data and making it available to each other and/or third parties, etc.

According to the European Commission, contracts should be “built on trust” which is an essential prerequisite for all private sector data sharing. Thus, rights to access and (re-)use data should be subject to conditions clearly defined in the data sharing agreement.
Such contracts should determine in an explicit and transparent manner who has the right to access, (re-)use, and distribute data.
In the Staff Working Document “Guidance on sharing private sector data in the European data economy”, the Commission specified five guiding principles for agreements conducted within B2B data sharing platforms:
(i) transparency; (ii) shared value creation; (iii) respect for each other’s commercial interests; (iv) ensure undistorted competition; (v) minimized data lock-in.
What each principle means for data sharing within the TRUSTS project will be analysed in the deliverable D6.2 Legal and Ethical Requirements, as part of a broader question about the applicable legal framework.

Stay tuned to know more about legal and ethical challenges that lie ahead of the TRUSTS partners.

Author: Lidia Dutkiewicz, KU Leuven (KUL), Belgium



Photocredit NASA on Unsplash

A Case for Data Markets?

With the ever-accelerating growth of data economy, one may expect a proliferation of data markets. Such marketplaces are multi-sided platforms where data sellers, data buyers, and 3rd parties can trade data and related services.
And what better to serve companies that are increasingly requiring external data sources to supplement internal data to innovate their products?
However, over the past 10 years, private commercial entities have tried but by and large failed to set up commercially viable data markets, bar the exception of certain specialised niche markets or atypical markets taking a vastly different role than that of a mere agent.
It appears that in the real world, organisations rarely trade (industrial) datasets on multilateral data markets. The reasons thereof are manyfold, but most often relate to (1) legacy architectures and data silos of potential data sellers,
(2) implications arising from a still nascent understanding and misconceptions of data as tradeable economic good, and (3) technological and regulatory constraints and hurdles in emerging data ecosystems.
Multi-sided Data Markets (DM) are more akin to ecosystems, i.e. a dynamically evolving amalgamation of regulations, standards, protocols, technology, data, services, entities, and transactions. A commercially viable Data Market Operator (DMO) may take an important role in such an ecosystem, yet this might not be the traditional role of a centralised commercial entity but the role of a federator and facilitator.
TRUSTS will act as agent and aspires to be both, a data market as well as a data market federator and custodian of a wider ecosystem.
The envisioned TRUSTS data market platform will build on a decentralised, open architecture. This not only helps to address fundamental privacy and security concerns but also acknowledges challenges of data ecosystem stakeholders in transforming their own legacy data silos. Furthermore, interoperability between common suppliers and buyers within the TRUSTS data market platform will be enriched by added interoperability with other (data market) platforms and 3rd party service providers.

Business model research within the TRUSTS project will need to identify how these principal technological capabilities, supplemented with additional responsibilities within the ecosystem, can be translated into commercial sustainability and contribute to untying the Gordian Knot of data markets.

Author: Bert Utermark, G1 Governance One, Germany


TRUSTS – requirements


Figure 1: Methodology for the requirements elicitation, analysis, and usage

The TRUSTS project follows a user centred approach, which places the stakeholders of a product or a system at the centre of its design and development. User centred design (UCD) seeks to answer questions about users and their tasks and goals. The answers to the questions are then used to drive the design and development. This is achieved by involving and talking directly to key stakeholders throughout the project, starting from its very beginning to assure that the platform will deliver the foreseen requirements.
Following the Ergonomics of Human System Interaction standard (ISO 9241-210), which is part of the multi-part standard ISO 9241 and a revision of the withdrawn ISO 13407:1999, outlines four essential activities in a user-centred design project:
  1. Requirements gathering – Understanding and specifying the context of use
  2. Requirements specification – Specifying the user requirements
  3. Design – Producing design solutions
  4.  Evaluation – Carrying out user-based assessment of the TRUSTS platform
The TRUSTS task 2.2 focuses on the two first activities indicated above which deal with the collection, analysis, and specification of the requirements.
The means to collect the requirements are through:
  • key stakeholder interviews
  • the use of dedicated electronic surveys
  • the analysis of selective related data marketplace activities
  • the analysis of related legal framework
  • the analysis of the use cases
The context, in which the requirements are analysed, is the TRUSTS project objectives.
The methodology to produce and use the TRUSTS data marketplace functional requirements (FRs) is illustrated in Figure 1 above. In particular:
  • All requirements sources are analysed for individual requirements and their justification.
  • The requirements mentioned above drive the definition of the FRs, which will be used for the implementation of the TRUSTS platform as well as the operational processes design.
  • To assist implementation, each FR is mapped to the respective project task.
  • Furthermore, the FRs will be used by the methodology defined in task T2.3 Testing framework and benchmarking to evaluate and provide coherent feedback through the UC trials (Work package 5 – WP5).
This process is depicted in the implementation of the deliverable D2.2: Industry specific requirements analysis, definition of the vertical E2E data marketplace functionality and use cases definition I (first version, M6), as well as in the deliverable D2.3: Industry specific requirements analysis, definition of the vertical E2E data marketplace functionality and use cases definition II (Final version, M24), which will describe an updated version of the requirements.

The requirements collection process resulted in 44 FRs, which are part of the following categories:

  • Datasets and services onboarding functionality and processes
  • Intelligent data/service exploration and correlation functionality and processes
  • Purchasing and billing
  • (Meta-)Data Governance
  • Data as a Service and Subscribers management
  • Data protection
  • Advanced data analysis based on Machine Learning
  • Trusted and legitimate data flows

Deliverable D2.2 is a public document and will follow.

In this deliverable we analysed key sources to gain requirements with respect to the development and operation of an industrial data marketplace targeting the telecommunication and financial sector and beyond, following a comprehensive methodology.

In today’s interconnected world business silos seem to fade enabling business expansion to other domains and collaboration between various industries. A forward looking data marketplace endeavour such as TRUSTS aims at clearing the obstacles in the data exchange process by establishing a comprehensive platform which includes all respective best practices, standards, and regulations to address a wide variety of telecommunication companies, financial institutions, and their collaborators.

To this end, an extensive list of functional requirements’ specifications was produced indicating both the source requirement and the task that will undertake its evaluation and implementation.
Within this deliverable the three Use Cases (UCs) have been thoroughly analysed in terms of their needs with respect to the TRUSTS data marketplace. Their anticipated operation through the TRUSTS environment is detailed along with the roles, trials descriptions, high-level scenarios, and respective KPIs.
This set of information along with the functional requirements will be used for the evaluation of the implementation to improve the TRUSTS platform using the task 1.3 Methodologies.
This deliverable is the first version of the two reports, which include the detailed analysis of the requirements for a commercial, financial and operators’ industry vertical data marketplace platform and the use cases definition with the target KPIs that would set the benchmarking for the actual measurements.
The key strategic outcome of the analysis of the elicited requirements from all sources is that the overall TRUSTS objectives are in line with all the key stakeholders’ expectations thus setting the bar high for defining a successful service, which has a significant impact on the data industry.
Responses to the questionnaire together with the interviews provided a solid ground for the identification of the stakeholder requirements. The outlined FRs are technology agnostic since they do not aim to set the implementation framework but rather the required functionalities and processes of the TRUSTS data marketplace. Task 2.4 will define the architecture principles while the end-to-end environment will be tested through the UC trials.
Task 2.2 will continuously collaborate with the following Work packages (WPs):
  • all WP1 tasks to evaluate additional information with respect to the TRUSTS architecture and data marketplace initiatives and trends
  • WP7, which will produce adequate business models and receive market feedback from any related exploitation action
  • WP6, which will define the legal aspects and processes
  • WP3 and WP4, which will undertake the platform development
  • WP5, which will execute the UC trials providing valuable feedback and
  • WP8 to provide information and evaluate feedback from respective events
It is understood that the FRs and UCs aim at defining the functionality and operational requirements of the end-to-end platform. However, an analysis will be made in the technical WPs to evaluate which functionality can be implemented within the resources of the project. The implementation tasks aim at producing an environment that will be able to support all essential data marketplace functionalities. In any case, it should become clear that some functionality which is requested by the FRs cannot be fully addressed within the scope of the project and according to the provisioned resources allocation; this will be appropriately documented and reported, towards the goal of scheduling the implementation as part of the commercialisation phase.

This work will be thoroughly analysed in the deliverable D2.3 Industry specific requirements analysis, definition of the vertical E2E data marketplace functionality and use cases definition II, which is due in M24.With regard to advancing T2.2 activities and the implementation of the D2.3 deliverable, the project will assess the initial developments, identify additional stakeholders, and refine questionnaires to constitute the final set of the TRUSTS environment requirements.

Author: Ioannis Markopoulos, FORTHNET, Greece





If you would like to learn more about the EU-funded TRUSTS project, just follow the link below to access the recording of a journalists’ call on 7 September 2020.

It gives an introduction of the project and contextualises it with European policies.

Speakers: Ioannis Markopoulos (Forthnet, Greece) and Andreas Huber (Governance One, Germany).
Host: Nina Popanton (Data Intelligence Offensive, Austria)

Go to recording




TRUSTS in the financial industry – enabling data sovereignty beyond existing solutions

Webinar / 29 September 11:00
What does a data market need to make participants feel safe when sharing personal and proprietary data? In one word: trust.
On 29 September 11:00 CEST, experts from the TRUSTS project will present their efforts to set up a GDPR-compliant European Data Marketplace based on privacy, confidentiality and data sovereignty.
The live session is part of the series “views on IDS” hosted by the International Data Spaces Association.
In collaboration with the International Data Spaces Association, Alexandra Garatzogianni, the coordinator of the project along with Gianna Avgousti (eBOS), Christos Roupas (REL) and Benjamin Heitmann (FHG) will introduce the goals of TRUSTS.
The project aims at creating a data sharing platform for personal and industrial use by interconnecting different user groups and providing generic functionalities for innovative applications and services.
Author: Nora Gras, International Data Spaces Association (IDSA), Germany


Privacy Preserving Technologies for Trusted Data Space

October 1 @ 11:00 – 12:00 | Webinar, Free

Our consortium member IDSA is on a mission to bring sovereignty and trust to data ecosystems while the Reference Architecture Model (IDS-RAM) is offering a framework to leverage more data in a trusted way.
But how could it be executed in highly constrained environments? What can the technology could offer when data cannot be moved or disclosed at all?

Register here.



Women in Data Science Vienna

October 3 @ 10:00 – 16:30 | Free

BDO Austria Am Belvedere 4, Vienna
The Vienna Data Science Group and Women in AI organise the first Women in Data Science Conference in Vienna – the one-day event that aims to inspire, connect and bring more diversity into the field of data science.
Anyone interested in data science, no matter what professional background or gender, is invited.
Get your tickets here.

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European Big Data Value Forum 2020

KOSMOS Berlin Karl-Marx-Allee 131a, Berlin
The European Big Data Value Forum (EBDVF) is the flagship event of the European Big Data and Data-Driven AI Research and Innovation community organised by the Big Data Value Association (BDVA) and the European Commission (DG CNECT).
Industry professionals, business developers, researchers, and policy-makers coming from over 40 countries take part.

Learn more