Creating trustworthy European data space federations – challenges and lessons learned from TEADAL and TRUSTEE

When facilitating multi-disciplinary data exchange across data spaces, it is critical to preserve data privacy and control which users have access to the data and may perform actions on it, in order to aid transparency and stimulate collaboration across diverse data actors.

This session will include discussion topics that will focus on the following:
– What is the interplay between data platforms and data spaces?
– What are the main challenges involved in the design and development of data platforms that aim to foster data federation across data spaces?
– What is the current state of the art of Privacy Enhancing Technologies (PETs) relevant in the context of data sharing/exchange in data spaces and how can different PETs help achieve appropriate privacy levels in sharing data and in its secondary use?
– How to ensure trust in data ecosystems?
– How might the application of AI/ML solutions facilitate data manipulation in data spaces while preserving data privacy?
– How can the accountability of the actions conducted by the actors using the data spaces for data exchange and interaction be ensured?
– How can sustainability be fostered in data federation platforms?
– Are there already agreed upon identifiers and description schemes of data sources among federators of different data sharing ecosystems? (e.g., GAIA-X)

TEADAL: The aforementioned questions will be addressed from the TEADAL perspective, which aims to create an easy-to-adopt way for cross-organizational efficient and effective trustworthy data sharing. This will be discussed in terms of architectural principles and components that are guiding the development of the TEADAL project. Notably, we present the adoption of the data mesh approach in a federated fashion and the role of blockchain for mediatorless access and transparency to create trusted federated data spaces.

TRUSTEE: During this sub-session we will discuss the main TRUSTEE architectural components that aim to enable trustworthy data federation and multi-disciplinary data use and secondary use across data spaces. TRUSTEE is closely connected to data spaces since it is a mediator that aims to unify and facilitate diverse uses of data across data spaces, encouraging a new way of data handling while ensuring privacy of a provider’s data and providing end-users with the ability to use data in an encrypted form. Through TRUSTEE, a secure and privacy-aware computing platform is proposed, which incorporates Privacy Enhancing Technologies (PETs) and Self Sovereign Identities, among others, for enabling querying, light computations, and AI applications across multiple data spaces concurrently