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

跨学科、地理上分散的数据存储库的出现在我们这个数据驱动的时代是不可避免的。事实上，这些存储库不一定遵守现有的跨学科数据表示标准，也不一定参与任何数据联合倡议，这使得它们无法使用，因为研究人员无法随时访问这些数据集。此外，在这样的交互中保持完整性、隐私性和安全性通常是极其困难或不可能的。为此，TRUSTEE旨在开发一个安全、可信和隐私感知的框架，该框架将聚合不同的跨学科数据存储库，如医疗保健、教育、能源、空间、汽车、跨境等，并考虑到其他欧洲数据联盟空间和跨国倡议，如Gaia-X和EOSC。TRUSTEE将提供一个安全的设计框架，其中数据是同态加密的，从而为研究人员提供i）在加密域中搜索和使用数据的能力，ii）以开放和公平的方式对数据进行统一和有意义的公平表示，iii）通过高级本体进行复杂和上下文感知的查询，iv）通过透明的可信ML工作流进行数据处理和分析，而不是一个直观的AI游戏场，这将促进AI的可解释性。支持GAIA-X等的六个不同用例将验证TRUSTEE的完全加密解决方案，展示多学科，泛欧联邦FAIR和私有数据生态系统。

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.