TRUST AND PRIVACY PRESERVING COMPUTING PLATFORM FOR CROSS-BORDER FEDERATION OF DATA

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The emergence of interdisciplinary, geographically dispersed data repositories is inevitable in our data-driven age. The fact that these repositories do not necessarily adhere to existing interdisciplinary data representation standards and do not necessarily participate in any data federation initiative renders them unusable, as researchers are unable to readily access these datasets. In addition, maintaining integrity, privacy, and security in such interactions is typically extremely difficult or impossible. To this end, TRUSTEE aims to develop a secure, trustworthy, and privacy-aware framework that will aggregate diverse interdisciplinary data repositories, such as Healthcare, Education, Energy, Space, Automotive, Cross-border, etc., as well as take into account other European data federation spaces and trans-national initiatives, such as Gaia-X and EOSC. TRUSTEE will provide a secure-by-design framework in which data is homomorphically encrypted, thereby providing researchers with i) the ability to search and use data in the encrypted domain, ii) a unified and meaningful FAIR representation of data, in an open and fair manner, iii) complex and context-aware queries through advanced ontologies, and iv) data processing and analysis through transparent trustworthy ML workflows, over an intuitive AI playground that will promote AI explainability. Six distinct use cases supporting GAIA-X, etc. will validate TRUSTEE's fully encrypted solution, demonstrating a multi-disciplinary, Pan-European federated FAIR and private data ecosystem.