Big Data PPP Industrial Data Platforms - Towards cross-sectorial optimization and traceability To start identifying synergies and to learn how different projects will address key data collection, sharing, integration, and exploitation challenges, a series of webinars have been organized under the umbrella of this Big Data Value PPP. These webinars are also organized by BDVA, BDVe project, and other projects which are part of this PPP.

1. This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 871481

2. TRUSTS, Trusted Secure Data Sharing Space
Innovation Action (IA), H2020 ICT-13-2018-2019
Supporting the Emergence of Data Markets and the Data Economy
January 2020 – December 2022

3. Challenges
Lack of trusted &
secure platforms &
privacy-aware analytics
methods for secure
sharing of personal
data
Proprietary,
commercial, industrial
data hampers the
creation of a data
market & data economy
by limiting data sharing
mostly to open data
Diverging technical
standards, quality
levels & legal aspects

4. Reinstate trust previously placed in the data market by
developing a new platform using the experiences of two
large national projects
Fully operational & GDPR-compliant European
Data Marketplace for personal & industrial use
TRUSTS will:
Allow the integration & adoption of future platforms.
Act independently & as a platform federator
Investigate the legal & ethical aspects that apply on the entire
data value chain, from data providers to consumers

5. Data market
as a shopping mall for data
• advancing technology foundations for
secure data markets & cloud
interoperability
• creating an environment encouraging
data-centred innovation
The Data Market Austria Project established
a Data-Services Ecosystem in Austria by

6. https://datamarket.at/

7. Sustainable building
block for ecosystems
& data economy
P2P global dynamic data &
business transactions
between participants across
all domains, sectors &
industries
Capable of linking single
objects up to entire platforms
Basis for data ecosystems &
market places based on
European values, i.e. data
privacy & security
Equal opportunities via
federated design

9. Fully operational & GDPR-compliant
European Data Marketplace for
personal & nonpersonal related data
targeting both personal & industrial
use by leveraging existing data
marketplaces & enriching them with
new functionalities & services
Demonstrate & realise the potential of
the TRUSTS Platform in 3 use cases
targeting the industry sectors of
corporate business data, specifically in
the financial & telecom operator
industries while ensuring it is supported
by a viable, compliant & impactful
governance, legal & business model
1
2
Main objectives

10. Robust legal &
ethical framework
for the TRUSTS
Platform to ensure
sustainability &
compliance with
all relevant
regulations &
ethics principles
Sustainable
business model &
plan (incl. products
& service portfolio,
clear SLAs, pricing,
billing etc.) for the
TRUSTS Platform
supported by a
wide-reaching Data
Innovation
Environment
Expected key innovations
(incl. timeline)
1 2

11. End-user requirements, needs, challenges, trends
Functional requirements & architectural design
Multi, concurrent & cross-domain, secure &
scalable E2E data marketplace service
Industry-specific functional specifications (M6)
KPIs & methodologies for demonstration & validation (M6)
Architectural design & technical specifications (M12)
1

12. TRUSTS Infrastructure set-up & continuous
improvement (M12)
Data Marketplace:
Smart contracts & Interoperability solutions
(M30)
TRUSTS Platform integrated & deployed in 3
releases (M12, M24, M36)
2) TRUSTS Platform implementation
2
Leverage existing data markets technologies & components (DMA, IDSA)
Integrate them in the TRUSTS Platform

13. Novel algorithms for Privacy-Preserving Data Analytics
developed & integrated (M18)
Innovative Deep learning algorithms on distributed
frameworks developed for the use of compute-intense
neural networks over several nodes under the TRUSTS
platform (M30)
New algorithms integrated & operational in the TRUSTS
Platform (M36)
Secure data sharing & federated data processing
Cryptographically secure protocols for data
analysis of privacy-sensitive data
Privacy preserving technologies
3

14. Demonstration of the TRUSTS platform in
3 business oriented UCs
Showcase the sharing, trading, (re)use of data & services
4
Implementation & testing plan for pilots (M14 , M25)
1st phase of UC trials (M24)
2nd phase of UC trials (M32)
360° performance evaluation (M25, M33)

15. Requirements & guidelines (M10)
Legal & ethical assessment (M33)
Legal & policy recommendations for
sustainability & exploitation (M36)
Sustainability & compliance w/ all
relevant regulations & ethics principles
Legal & Ethical Framework
5

16. Taxonomy of data marketplace business models (M12)
Community engagement (M18)
Standardisation activities (M24, M36)
Viable, feasible & sustainable business models (M36)
Sustainable business model & plan for the Data-Services Ecosystem
(incl. products & services portfolio, clear SLAs, pricing and billing etc.)
Business Model, Exploitation & Innovation Impact Assurance
6
Standardization of data sharing platform
Innovation, commercialisation
& IPR management

17. Balance between
• (non)-economic interests of multiple stakeholders
along the digital value chain
• regulatory frameworks for producers and
consumers of data (i.e. the GDPR, consumer
protection law, unfair commercial practices
directive)
• entities active on the DSM w.r.t. industrial data (i.e.,
regulation on the free flow of non-personal data,
platform regulation, competition law, intellectual
property law, the AML directive, codes of conduct).
• variation of regimes of ownership in different
jurisdictions (by individuals, by entity that claims
rights over the product of their processing efforts as
data with added value)
UCs & key characteristics of data sets involved
Data subjects & data owners : in control of their data & subsequent use
• mixed nature data (i.e., personal & industrial/non-personal
• industrial data: shared & traded in compliance with legal rights & fair remuneration
of data owners

18. 3 business oriented use cases:
Anti-Money Laundering compliance
Agile marketing through data correlation
Data acquisition to improve customer support services
Goals:
Showcase sharing, trading, (re)use of data & services
Result in added value generated via innovative applications
built on multiple open & proprietary data sources

19. Faster & more accurate detection of financial
crime & money laundering
Secure brokerage of enriched data via the
Platform to interested customers who perform
AML checks e.g.
Anti-Money Laundering (AML) compliance
• financial institutions
• internal corporate audit
departments
• fiduciaries
• corporate service providers
• tax advisors
• automotive dealers
• estate agents
UC1
Data shared via the Platform integrated in an existing AML
solution enhanced with big data analytics
Goal:

20. Types of data
open-source, closed-loop,
open/ public, validated,
realistic anonymized /
pseudonymised data
Business impact
Better evaluation of risk
score/assessment
Better detection accuracy
Reduced compliance costs
Efficiency, competitiveness
VS financial crime
TRUSTS Services
Existing AML solution (WiseBOS)
enhanced w/ big data analytics
Accurate detection of financial
crime & money laundering
Secure sharing, brokerage /
trading of enriched data for AML
compliance

21. Advanced marketing activities via
correlating anonymized banking & telecomm data
Agile marketing through data correlation
UC2
Establish & validate how big data analytics
techniques applied on data shared via the TRUSTS
Platform can provide timely & meaningful
information towards targeting customers at
a local level
Goal:

22. Types of data
GDPR anonymized
financial, banking &
telecomm data
(CRM) TRUSTS Services
Anonymisation
Protection from
deanonymisation attacks
Data synchronisation
Up-to-date data sets
Data valuation, correlation
Big data analytics for
marketing Business impact
GDPR compliant marketing
Smart big data analytics for
(local) marketing analysis
Reduced costs

23. Out-of-the-box analytics solution for anonymisation
& visualisation of Big Financial Data
Data acquisition to improve
customer support servicesUC3
Advance new ways of HCI
e.g. chatbots as automated assistants to allow
customers to converse about the management
of their debt at their own pace w/ a personalized
experience, via integration of Big Data
Goal:

24. Types of data
Customer data
(anonymization &
cryptographic
protocols) TRUSTS Services
Anonymisation
Protection from
deanonymisation attacks
Data synchronisation
Data masking
BD analysis & correlation
Chat bot
Ready to market NL &
semantic components
Business impact
Cognitive computing
(NLP/BD/AI/HCI)
Robo-advisors / chatbots
Scalable tailored wealth
management services
Debt collection
BD visualisation

26. Business (commercial,
operational, legal,
standardization):
developing & testing
innovative business
models & the effects of
current, future
regulations, as threats
and incentives for data
enterprises
Impact & Growth Economy
Technology:
by providing a new
state-of-the-art for
specific challenges such
as that of a secure
platform, vitally needed
for different data
providers to interact
confidently &
successfully in a market

27. Knowledge-based
business decisions
Efficiency, automatic
decision making,
predictive models, robo
advisers: improve quality
of knowledge-based
business decisions
Business impact
Fairness & transparency
Data-driven, viable,
feasible & sustainable
business models
(customisable & self-
sustainable)
Business validation of
UCs & Platform

28. A Federated Data Market at European level shall provide:
- Hierarchical levels of privacy allowing data owners full control
at granularity & metadata level
- Hierarchical layers of certification for data services
- Flexible combination of data & services available at different
providers in order to create a new data product or service
- Automatic brokerage system
- Tooling for a human broker to create customized offers
Per ser & as platform federator
Lower the barrier to entry for e.g. private
entrepreneurs, innovators, SMEs or NGOs, to
large, multinational enterprises

29. Lack of privacy-aware
analytics methods for
secure sharing of personal
& industrial data
Scalability, computational
efficiency of methods to
secure desired levels of
privacy of personal data
and/or confidentiality of
commercial data
Analyse & address
privacy/confidentiality
threat models and/or
incentive models for the
sharing of data assets
Multiparty computation protocols
Building on existing platforms by adding zero-
knowledge encryption
New cryptographic low multiplicative complexity
primitives improving the computational
efficiency of state-of-the-art private-set
intersection (PSI) & multiparty computation
(MPC) protocols.
Guaranteeing protection of their own data &
allowing joint computation to be performed on
their independent data sets
Privacy/confidentiality threat models will be
developed/adapted from the multi-actor
perspective (i.e., users and organizations
procuring safeguarding technologies, UCs)
Privacy/data protection & utility

30. 1
Technical
Data
Challenges
2
Legal
3
Business

31. • IT standardisation: technologies converge & federated
systems arise, creating new gaps in interoperability
• Multiparty computation: practical implementations &
instantiation
• Quickly set-up digital support for such data value chains in
an increasingly dynamic manufacturing ecosystem, while
addressing key challenges, e.g. semantic interoperability,
security in cross-domain setups, findability of data sources,
entity linking, ensuring data quality & commercial
confidentiality
Challenges faced regarding data: Technical

32. • Big Data, AI/ML techniques on banking services & policies to
ensure that consumers do not suffer any detriment
• Automated decision making activities (incl. emotion-detection
techniques) on consumer behaviour
• Smart contracts: e.g. validity, enforceability, interpretation
• Privacy & Data Protection: GDPR, e-Privacy Directive &
forthcoming Regulation, especially legal & ethical challenges
around privacy-preserving techniques, Big Data analytics &
automated decision making
Challenges faced regarding data: Legal
• Commodification of personal data w.r.t. rights &
obligations vis-à-vis data subject rights & market players
in the DSM (e.g. data sovereignty, data ownership)

33. • EU & worldwide challenges & trends for data-sharing
• Secure platform, vitally needed for different data providers
to interact confidently & successfully in a market
• Advanced marketing techniques relying on big data
analytics, e.g. the Unfair Commercial Practices Directive
• Intellectual Property Rights (IPR) & Data Stewardship (DS)
Challenges faced regarding data: Business
Ensure an environment of trust & accountability through a
predictable legal environment for businesses & investors while
safeguarding the rights of consumers & citizens.

35. trusts-data.eu
@TrustsData
TRUSTS Trusted Secure
Data Sharing Space
Alexandra.Garatzogianni
@tib.eu
@AlexandraGaratz
https://www.linkedin.com/in
/alexandragaratzogianni/
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 871481