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G1 and TU Delft conducted a workshop within the internal consortium on 22 October 2020 to discuss the positioning of the TRUSTS-project in the European data economy. The objective of the workshop was twofold:

1. Solicited perspectives and inputs from TRUSTS consortium partners to position the TRUSTS project in the European data economy
2. Initiated business modeling for trusted secure data sharing spaces

Participants discussed three possible choices of TRUSTS positioning in the European data economy. These are 1) TRUSTS as a data market, 2) TRUSTS as a federator of data markets, and 3) TRUSTS as a data ecosystem facilitator.

First, as a data market, TRUSTS can be viewed as a platform to facilitate data trading (i.e. data exchange and financial transaction) between data providers and data buyers. It will provide fundamental functionalities such as onboarding mechanisms, dataset discoveries, trading arrangements, etc. To date, TU Delft has developed two business model taxonomies to position TRUSTS as a data market (see Bergman, 2020; van de Ven, 2020). The studies have identified value-adding services to attract both data providers and buyers, namely:

1. cleaning data to ensure data quality
2. analysing data to create aggregated datasets
3. offering personal assistance in data sale and acquisition

© mark-fletcher-brown on Pixabay

Second, TRUSTS can also be viewed as a federator of data markets, going beyond the development  of “yet another data market platform.” As a potential federator, TRUSTS will be interoperating with existing data markets (IDS, DMA) and future data markets. TRUSTS will potentially create value by forwarding and connecting data market traffics, gradually  harmonising technology stack through coordination and common standards, and providing non-differentiating capabilities (e.g. billing) as shared-services. One interesting topic raised by a participant was the conceptual form of the federation.

“Which conceptual model will we employ? The federation based on a hierarchal meta-platform or Peer to Peer (P2P) network of data markets?”

The participants discussed that a hierarchical meta-platform would be a starting point for the federation of data markets. Insights from such a meta-platform as well as insights from interoperable industrial data clouds may lead to the emergence of P2P federations of data markets in the more distant future.

Finally, as a data ecosystem facilitator, TRUSTS will be potentially served by taking on one or multiple roles as

• a champion among data markets and propagating federatedness
• an industry association of European data markets by driving standards and shaping policies,
• an innovation hub, providing experimental environment(s) and brokerage of associated services

In general, the workshop participants were aware of these three positionings and had the same perspective that TRUSTS should fulfill the project mandate (i.e. enable interoperability with other data markets). Other interesting topics that need to be further discussed include the identification of TRUSTS’ value proposition, the primary obstacles to the adoption of federation by data markets, the operating company of TRUSTS, etc.

The next step is to develop webinar content based on these workshop results. We will also refine the workshop materials and re-run the refined workshop with participants outside the TRUSTS consortium.

Reference

BERGMAN, R. 2020. A Business Model Taxonomy for Data Marketplaces. Master of Science, Delft University of Technology.

VAN DE VEN, M. R. 2020. Creating a Taxonomy of Business Models for Data Marketplaces. Master of Science, Delft University of Technology.

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December 7, 2020 – Leuven/Vienna. Privacy and data protection supporting the compliance of data sharing with EU rules is a new challenge for our society. A specific legal framework that protects relevant data from unauthorised use but also does not put European businesses at a disadvantage in global competition, is urgently needed. That is what TRUSTS is working on. In the project’s deliverable 6.2. (Legal and Ethical Requirements) the issue of controllership of personal data, the ensuing allocation of data protection responsibilities and the legal basis for processing personal data are determined. It informs about the main concepts of the ePrivacy legal frameworks and their relation with GDPR and provides further conceptual legal information on privacy-preserving techniques that might be relevant for TRUSTS partners, stakeholders as well as the European data community.

The legal patchwork for data transactions

“Industry players are increasingly considering data as a commodity to be traded in their own rights. This is particularly seen in data markets. In addition, there is a growing interest of policy makers in data markets and the exchange of data across industries. Still, the legal framework does not sufficiently address data rights in this respect towards commodification of data,” states Lidia Dutkiewicz, researcher at the Centre for IT & IP Law, KU Leuven.

The legal frameworks have increased over time with various rationales. In contrast, the question of whether and how data could be turned into a tradeable asset is  rather new. Even data as a subject matter is a rather new construct in legal thinking. The patchwork of legal frameworks applying to data (transactions) might make it more difficult to trade data because of uncertainty that it brings about and because of the context-specific nature of legal analysis. This being said, a great array of options can be arranged by transacting parties by means of a contract. “However, a major issue lies in the fear of data providers to lose control over their data upon sharing them. Given the absence of clear rights on data, we will further investigate whether data sovereignty can be a solution without hindering core legal principles,” adds Charlotte Ducuing, researcher at the Centre for IT & IP Law, KU Leuven.

© jpornelasadv on Pixabay

Data markets: step by step into a secure and sovereign future of data usage

Charlotte Ducuing, Lidia Dutkiewicz and Yuliya Miadzvetskaya from the Centre for IT & IP Law, KU Leuven (www.kuleuven.be), analysed the European laws and regulations relevant to the TRUSTS platform, defined legal and ethical requirements and identified potential legal and ethical obstacles. The emphasis of this deliverable was put on the most relevant legal and ethical aspects regarding data markets, data sharing, etc. This should enable important pioneering steps to be taken towards a future with sovereign data use. (see full text of deliverable at the end)

On the one hand, there is no ownership right in general on data in the EU member states. Many of the consulted legal scholars also consider that such an ownership right should not be set up due to the specific nature of data. Therefore, data cannot be sold legally like a table or watch. On the other hand, a variety of legal frameworks may apply to data and data transactions, which is subject to context-specific analysis. To provide a little insight: data protection law is applicable when personal data are processed, copyright protection may apply as well as the legal protection of databases. The dataset may be legally protected as trade secret or otherwise be subject to contractual arrangements such as non-disclosure agreements or limitations of (re)use. In addition, sectoral legislation may determine or lead to various types of obligations relating to data directly or indirectly, including an obligation to provide access for third parties to reuse data or alternatively confidentiality or secrecy obligations, etc.

Strong demand for research remains

Yuliya Miadzvetskaya, researcher at the Centre for IT & IP Law, KU Leuven, concludes: “Whether and how the EU should intervene to regulate data sharing from a legal perspective needs to be further analysed. This lies at the core of TRUSTS interdisciplinary research”.

Please find D6.2 in full length at: https://www.trusts-data.eu/deliverables/

Link to CORDIS

Link to APA OTS

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The document draws inspiration from the FAIR data principles that affirm that data should be findable, accessible, interoperable and re-usable. As follows from the analysis in deliverable D6.2, the current framework for data sharing is a patchwork of various legislative instruments. The lack of coherence, consistency and certainty hinders data sharing across the EU. With the Data governance act, the Commission now seeks to stimulate data sharing and re-use by strengthening trust in data intermediaries and data exchange mechanisms.

The choice of the legal instrument – regulation – is not accidental since in contrast to directives it does not leave margin of implementation to the member states and thus contributes to a better coordination and homogeneity of EU data policies. Risks of divergent interpretations and implementations across the EU are eliminated or at least minimised.

Four areas for EU intervention are identified:

• mechanisms stimulating the use of public sector data which is not open data
• a certification framework for data intermediaries
• measures facilitating data altruism
• an EU-level structure ensuring the coordination of the horizontal data governance framework

© Gerd Altmann on Pixabay

To bring trust to the functioning of data intermediaries, the certification framework will be put in place on a semi-compulsory basis. The monitoring of the activities of data intermediaries is foreseen by the competent authorities of the Member States upon notification. However, the efficiency of certification as a mechanism to bring trust is questionable. It does not seem that it was a considerable game changer under the GDPR or the EU Cybersecurity Act. It remains to be seen whether it will help to cultivate the culture of trust for data intermediaries or Data Altruism Organisations. The understanding of the concept of trust in this context is particularly relevant for our future work in the TRUSTS project which relies on trust as one of the key elements for stimulating data sharing and re-use.

The draft regulation provides for the creation of a European Data Innovation Board meant to ensure the exchange of information and best practices by Member States’ authorities on data altruism, data intermediaries and the use of public data that cannot be made available as open data.

The proposal also identifies certain technical and legal procedural requirements for making available the categories of data held by public bodies that are not open data.

The draft regulation will go through the ordinary legislative procedure and a number of amendments will be certainly tabled by the European Parliament and the Council.  Regardless of how the final legal act will look, it is obvious that the establishment of common rules and practices in the Member States relating to data governance is only a question of time and negotiations.

Author: Yuliya Miadzvetskaya, KU Leuven

Learn more about the Data Governance Act of the European Commission.

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Sebastian Steinbuß, CTO, IDSA

Interview with Sebastian Steinbuß, CTO at IDSA 

November 27, 2020 – A publication of this interview or contents therein can only be published providing a reference to the TRUSTS project and informing TRUSTS’ communication lead, Nina Popanton.

Dear Mr. Steinbuß, thank you very much for taking your time. Just to give our readers a short introduction: you are the CTO of the International Data Spaces Association (IDSA) and responsible for the organization of the IDSA working groups as well as the technical development of the IDS, which is a data sharing scheme including a reference architecture and a set of agreements to be used for creating and operating virtual data spaces. This makes you an experienced expert with regard to the possibilities of efficient data usage for businesses and all the challenges that come with it – which already leads to my first question:

How can companies realize a secure and efficient data exchange?

Sebastian Steinbuß: For data to be turned into new products or smart services, companies must be able to capture, store, process, evaluate, and publish data efficiently and reasonably, including a link to data from others. What prevents many companies from data sharing is a lack of infrastructures and agreements that ensure data sovereignty for both the data provider and data consumer. IDSA solved this problem: Together with politics, research and industry, IDSA designed a reference architecture for secure data spaces. The participants of such a data space decide how they use their data and with whom they exchange it. The IDS standard defines binding rules for data exchange.

How is IDS connected to TRUSTS?

Sebastian Steinbuß: TRUSTS aims at creating a European Data Market based on sovereign, secure and trustworthy data exchanges. The TRUSTS platform will act independently and as a platform federator, while investigating the legal and ethical aspects that apply on the entire data value chain, from data providers to data consumers. Still, it is no start from scratch since it will be based on the IDS reference architecture.

What makes the IDS reference architecture so valuable for the project?

Sebastian Steinbuß: The IDS Reference Architecture Model is considered the de facto standard for creating and operating data ecosystems. Its approach is to enable interoperability through semantic data descriptions, to create trust between participants through certified security capabilities, and to establish governance rules for data usage and data flows. The IDS ecosystem allows data flows between all kinds of endpoints (e.g. instantiations of the International Data Spaces Connector). Semantic data descriptions provided by data endpoints are published at dedicated Meta-Data-Brokers, allowing potential data consumers to search for and identify data that is relevant (semantics) and applicable (quality) for their particular purpose, and to assess in advance data’s affordability (price) and usability (restrictions and obligations).

What is an ‘IDS Ecosystem’?

Sebastian Steinbuß: Our key word is data sovereignty – the IDS architecture ensures data sovereignty for those who make data available in data ecosystems. This means that data owners always keep control how it is used. They decide who uses their data for how long, for which application, for how many times and according to which terms & conditions. The IDS Connector acts as a secure gateway for IoT and other data sources. Every connector can team up with other connectors to form a peer-to-peer network. As a consequence, data exchange can but does not have to take place via a cloud, in which the data would be stored with a third party provider, but directly between the companies involved. A data value chain consists of multiple peer-to-peer connections and all together sum up to a data-driven business-ecosystem

What makes IDS so special compared to similar technologies? Do any comparable technologies exist?

Sebastian Steinbuß: Participants and core components of IDS-based data ecosystems must provide a high degree of trust and security regarding the integrity, confidentiality and availability of information exchanged in the IDS. Therefore, using certified core components as well as employing certified technical and organisational security measures is mandatory for participating in the IDS. The IDS Certification Scheme defines security levels for components and the requirements to be implemented. IDS is not only a technological standard but also a universal legal framework to create data-driven business ecosystems. Key requirement to make this a reality is to have a set of rules and policies that govern it in a decentralised way. All these rules and policies are collected in the IDS Rule Book. It describes the technical, operational, and legal agreements to enable the IDS ecosystem to be fully working in a real-world scenario. The first edition of the Rule Book will be published this year.

Many thanks for the informative explanations and the illustrations – and especially for taking your time.

[END]

Further inquiries:
Nina Popanton
TRUSTS Communications Lead
Data Intelligence Offensive
+43 664 20 45 965
nina.popanton@dataintelligence.at
www.trusts-data.eu
www.dataintelligence.at

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TRUSTS Trusted Secure Data Sharing Space is a H2020 project, which builds on the experiences and technology of the Data Market Austria (DMA) and International Data Spaces (IDS) data market projects and aims at developing a data sharing platform for secure, trustworthy, and GDPR-compliant data exchange. In doing so, TRUSTS not only acts as data platform provider for data exchange but also as data market federator and data ecosystem facilitator.

The objectives of the project are:

• to create a secure and trustworthy European Data market for personal and industrial use by connecting different user groups and providing generic functionalities for innovative applications and services
• to identify and overcome legal, ethical and technical challenges of cross-border data markets to exploit the full potential of European data economy

Within the project, the current challenges are identified and tested in three use cases in the financial and telecommunications sector to be able to offer a viable, compliant and impactful governance, legal and business model that companies can adopt. TRUSTS will explore and develop its sustainability plan in open discussions with stakeholders from large, small, and medium-sized enterprises as well as with existing non-governmental organisations to adapt to their needs.

A European Strategy for Data

To remain competitive on a global level and guarantee data sovereignty the European Commission has developed a Data Strategy which aims at creating a Digital Single Market (DSM). This requires common European data spaces where high-quality data is used and flows across sectors while respecting European values, fair and clear regulations and implementing rules for access and data governance mechanisms. However, there are still some hindrances such as lack of European data processing and storage solutions, lack of reusable data available and lack of comprehensive data governance approaches and the fragmented single market. Moreover, citizens and businesses are sceptical towards data sharing. Therefore, enablers and common European data spaces are necessary. TRUSTS acts as such an enabler by offering a federated approach where the consortium partners work together and offer federated European solutions for data sharing. Through DMA a technical bridge was built and it is being realised in TRUSTS now. The Data Intelligence Offensive (DIO), for instance, addresses the topic of federation through data circles, in which a federated solution for data sharing is developed.

Study on data markets

A comprehensive overview of the data market environment helps achieve sustainable results for the TRUSTS project. Therefore, IDSA performs a market study in which a common knowledge base is built and insights into current trends in data markets and recommendations for the implementation of TRUSTS are provided. However, there are also some challenges faced during the study related to pricing mechanisms and data valuation, monetisation schemes, control over data, data quality and integrity as well as privacy and security. As a second step, the framework conditions are analysed in more detail – the EU Data Strategy, economical, technological and legal aspects.

Another big question is if there is a competition or coopetition. In this respect, questions like the following are analysed: How is the rivalry among competitors, What is the threat of new entrants? Or How powerful are data buyers?

Creating innovative data ecosystems – from vertical sectors to horizontal exchanges with trust-based technologies

The vision of TRUSTS is to become a focal point of data exchange and a new federated data ecosystem to be implemented in everyday business processes of companies while allowing the integration and adoption of future platforms. In doing so, it will offer a set of unified services with standardised interfaces incl. end-to-end technologies, end-to end services and end-to-end processes that function harmoniously to increase the potential for industrial change. As a consequence, the project enables migration from various silos to a connected economy and a comprehensive, transparent and legitimate data exchange.

However, is this sufficient for business sustainability? To see which data business models will be sustainable the current challenges are identified and tested in three use cases, where different stakeholders are involved.

Moreover, TRUSTS has three business roles – to act as a data market, data market federator and ecosystem facilitator. In the data market concept, data sellers and buyers are directly linked but also depend on infrastructure providers, data marketplace operators and owners, and third-party providers. As TRUSTS sees itself more than just a marketplace there are two additional roles. As a data market federator TRUSTS offers a hierarchical centralised meta platform where data marketplaces, open clouds, brokers, etc. interoperate. As ecosystem facilitator TRUSTS asks which additional roles need to be taken on to make the platform work and to help the wider data economy.

Technical foundation to enable and maintain data sovereignty

As already said, the data market infrastructure of TRUSTS is based on the experiences and technology of the DMA and IDS European data marketplaces. In addition, new elements find their place such as advanced matchmaking based on profiles and brokerage for users and organisations, data governance based on semantic technologies, and designs for interoperability with other data market places.

One of the most important technical goals of the project is to provide the components for privacy-preserving data analytics. For this purpose, it relies on the following building blocks, which will be integrated in a framework for adapting algorithms to preserve privacy and security:

• classifiers and transfer learning
• deep learning
• anonymisation and assessment of deanonymisation risk
• federated deep learning

Legal and ethical pillar – a patchwork of different legal frameworks

To guarantee legitimate data exchange and sustainable results KU Leuven looks at all relevant regulations and ethical principles to develop a solid legal and ethical framework for the TRUSTS platform.

Data markets are based on the commodification of data, however, the law does not consider it as it is quite a new area and a complex issue. It also poses a big challenge due to a patchwork of different legal frameworks that are often context-specific. This applies to both personal and non-personal data. Furthermore, new intermediary roles emerge but the question is: who is responsible for dealing with what? We have to clarify the roles and guarantee trust. Techno-regulation plays a significant role too, where data sovereignty and how smart contracts fit with the law, for instance, is tested.

To analyse the legal and ethical aspects KU Leuven looks at two complementary areas of research: the TRUSTS data market platform ecosystems and TRUSTS use cases. This leads to many different legal frameworks involved such as the data protection law, regulation of data as an economic asset, regulation of data transactions, financial law, blockchain, smart contracts and the law or ethical challenges associated with the use of AI systems.

Stakeholder engagement

To create an innovative and a sustainable business model beyond the lifetime of the project and exploit the full potential, the TRUSTS partners build up a community of stakeholders around the data ecosystem. For this purpose, the TRUSTS partners draft a model of a data ecosystem, define the key stakeholders as well as respective engagement tools and outreach activities, where results and good practices are shared with the stakeholders.

“A vital data economy and a successfully working Data-Services Ecosystem in Europe is one of the factors to enable and ensure sustainable employment and growth and thereby societal stability and well-being.” TRUSTS consortium

As a first step the data landscape (www.datalandscape.eu) is analysed – incl. data providers and users. The next step is to identify stakeholder categories such as Data and Service Provider, Data Consumer, EU Proposal/Project, Competence Center/Digital Innovation Hub, or Technology Platform, and map it with the target group per work package. The results of the stakeholder mapping will be added to the TRUSTS website in form of a landscape map.

Everyone is invited to join the TRUSTS community and contribute to the data economy, just get in touch with us at info@trusts-data.eu.

The next webinar will be at the end of January 2021 and will deal with the topic of TRUSTS business model development in more detail.

Stay tuned and subscribe to our newsletter at the top-right hand corner of the website.

Here you can access the PPT slides and the recording.

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Privacy-preserving analytics – cryptographic methods

Generally speaking, cryptography is a way of encrypting data to protect it from unauthorised access and to transmit and store it in a secure way. Due to limitations of classical cryptography and data markets getting gradually more complex new methods or protocols are indispensable. One of the greatest breakthroughs in modern cryptography is the ability to perform calculations directly on encrypted data. This means that the true content of data is never revealed to other parties and stays fully private, although another party, e.g. a server or cloud, can perform calculations on the encrypted data. Two new protocols that allow such privacy-preserving analytics are Multi-Party Computation (MPC) or Fully Homomorphic Encryption (FHE).

Multi-Party Computation

In many business processes usually a trusted third party plays the role of an intermediary in case the other two parties don’t trust each other. In cryptography we usually want to get rid of such trusted third parties and ensure save and secure data exchange without the need of any middleman or mediator. MPC protocols allow that users can compute a mutual function or output without disclosing their input data, i.e. no user knows the input of the others but all can benefit from the calculation based on the combination of their data. To give an example let’s have a look at the use case of collaborative learning. Several parties want to use a machine learning (ML) model, which will work best if they all combine their data, but the users don’t want to actually share their data to others or competitors. This is where MPC comes in, it allows to solve this problem by keeping the input fully private and nevertheless still allows to perform calculations based on the complete data since all parties participate in the computation.

Computation on encrypted data

Let us assume you encrypt two numbers. If you want to add or multiply the encrypted versions, this will not work since you cannot do arithmetics with gibberish expressions. This is why the development of Fully Homomorphic Encryption (FHE) was such a big breakthrough. This novel method allows to do exactly that, to add the two gibberish expressions and if you decrypt the result (another encrypted expression) you get the same number as if summing up the numbers in plain text. In this way, you can outsource calculations to a server or cloud without having to share any data, the true content remains hidden and fully secure and only you as data provider have the key to unlock the results.

Limitations and challenges

Although the cutting-edge cryptographic and privacy-preserving protocols are very promising there are still limitations and problems of inefficiency that we have to overcome. In case of MPC, there is a huge increase of communication between the involved parties and also the corresponding security guarantees influence the performance. In case of FHE, there is again a huge performance overhead and the calculation on encrypted data is, at the moment, much slower than on unencrypted data (100 to 1000 times). This depends very much on the actual problem and number of necessary calculations and also faster solutions are possible, but it works as a rule of thumb. However, there is a lot of research going on right now to improve these protocols.

Federated Learning

When it comes to Federated Learning (FL) you also do not directly share your data with others, but again a global machine learning model can be trained on a server or cloud, where only the parameters of the model are exchanged between server an clients. First you download the shared model, update it by running the model locally on your device and sending only the updated parameters back to the central server where they are aggregated and combined with other results. Then the improved model is sent back to the devices again and you do the next run locally on your device. In other words, thanks to FL you can collect experience from a wide range of local datasets spread across different locations. This allows that multiple parties can collaborate on the development and training of models again without having to share their data.

Quantum computing – what does Schrödinger´s cat have to do with it?

Jumping from cryptography and federated learning to quantum computing may seem a bit abrupt, but there are several links to this exiting new technology. Especially if you ask the question on how to keep your data secure in the next decades. Let’s start with the main ingredients that are needed to build a quantum computer. The basic idea is to use quantum mechanical effects for the calculation of specific problems – a concept that was already introduced in the 1980s by Richard Feynman. First you need a physical two-state system that can create a so-called qubit and where the two possible states can form a superposition. Superposition is also what you might know from Schrödinger’s cat, a state where two possibilities occur at the same time. Then you need a second ingredient from quantum mechanics called entanglement, where you connect two or more of such qubits in a way that they are not independent from each other anymore but form one single quantum state. We cannot experience this in our every day life, this is again a true quantum feature of nature. The last part of a quantum computer is a way to interact and communicate with the entangled qubits, where usually interference (e.g. of microwave impulses) is used. The huge advantage of quantum computers is that N qubits describe 2^N possible states at the same time. So by manipulating the entangled system you can massively perform parallel operations on all states. This is why they can perform calculations with an incredible speed compared to classical computers – John Preskill, one of the quantum computing pioneers, formed the term quantum supremacy. Last year in a Nature publication there was a first claim that quantum supremacy has been reached, by performing a calculation on a quantum computer in 200 seconds, which would have taken 10 000 years on a super computer. There was some debate about that immediately after the publication, it was at least a very important milestone that showed the great possibilities of quantum computers. Given a large enough quantum computer (large means with a high number of high-quality qubits) there are algorithms that can break modern cryptographic systems based on factorisation and such machines would also revolutionise machine learning and AI. The threat of breaking crypto-systems like RSA with quantum computers also lead to a new research field called post-quantum cryptography, where new methods and algorithms are currently developed that are still secure – even if large scale quantum computers are available. One of these methods is FHE by the way – one of the mentioned links to the cryptographic methods described at the beginning of this post.

Where are we now?

First quantum computing applications are already used in several industries such as the automotive (quantum traffic optimisation, research on solid state batteries, etc.), medical and pharma (drug discovery, disease evolution), or finance (derivative pricing, portfolio selection and optimization, etc.) industry. However, quantum computing is still in its infancy. There are still many problems to solve and the current machines are far away of breaking RSA. The interaction with the environment usually destroys quantum properties, so quantum computers need to be isolated or cooled down to minimise such interactions. In physics this is called decoherence, which also solves the conundrum of Schrödinger’s cat by the way. However, new algorithms can already be developed now and especially hybrid methods where classical machine learning is combined with quantum algorithms have a lot of potential. First use cases already exploit advantages with the currently available quantum computers and although quantum computers will not replace classical computers as we know them we can expect fantastic new possibilities in the upcoming years.

Link to recording.

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Moderator: Martin Kaltenböck, SWC

Technical support: Thomas Thurner, SWC

This webinar is the start of our webinar series focusing on different aspects of the project.

Trust is a crucial precondition so that people feel secure and are willing to share their data to contribute to a pan-European data ecosystem in which European values like personal rights and GDPR are highly respected.

In this session you will learn more about the EU project TRUSTS within the H2020 programme and an Austrian perspective on it. You will be introduced to:

• EU Data Strategy
• A market study of data markets
• Creating innovative data ecosystems with interconnected resources that can function harmoniously as a single transparent unit
• Operator business model options in a federated TRUSTS data ecosystem
• Legal and ethical pillars of TRUSTS
• Technical foundation of TRUSTS to enable and maintain data sovereignty
• Stakeholder engagement

Please register here.

The post The core of TRUSTS: Innovating European data markets through trust, security, and federation appeared first on TRUSTS.

• What is the project all about?
• What are the motivations, objectives, goals but also risks?
• Is there a reference architecture on which TRUSTS is based?
• Are there already existing data markets? Do they play a role within the TRUSTS project?
• How can data markets be connected? How could such a connection be implemented technically?

Interview Partner: Stefan Gindl, Research Studios Austria

Interviewer: Nina Popanton, Data Intelligence Offensive

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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

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

Photocredit NASA on Unsplash

A Case for Data Markets?

TRUSTS – requirements

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

• 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 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.

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

Events

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

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

Learn more

European Big Data Value Forum 2020

Learn more

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