TITAN will enrich the EOSC Interoperability Framework (IF) with a software platform solution for confidential data collaboration and secure and privacy-preserving data processing. The platform will enable access to sensitive data sets from public entities and government agencies and will be compatible by design with the EOSC IF on the technical, semantic, organisational and legal layers. To promote community adoption of TITAN’s open-source software artefacts, the solution will be practically demonstrated in several vertical cross-border scenarios - notably in the public administration and healthcare sector

As we live in a data-driven era, the emergence of interdisciplinary, geographically dispersed, data repositories, is inevitable. The fact that these repositories do not necessarily abide with existing interdisciplinary data representation standards, nor do they necessarily belong to any data federation initiative, renders them unusable, since researchers cannot easily access this data. Moreover, most of the times, integrity, privacy, and security in such interactions is either very difficult, or impossible to maintain. Towards this end, TRUSTEE aims to bring a green, secure, trustworthy, and privacy-aware framework that will aggregate various interdisciplinary data repositories, such as Healthcare, Education, Energy, Space, Automotive, Cross-border etc. and also consider other European data federation spaces and trans-national initiatives, such as Gaia-X and EOSC. TRUSTEE will offer a secure-by-design framework, wherein stored data is homomorphically encrypted, thus offering researchers i) 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, iv) data processing and analysis through transparent trustworthy ML workflows, over an intuitive AI playground, which will promote AI eXplainability, interoperability, and re-usability, by utilizing state of the art methods and paradigms, v) compliance with European privacy and ethical frameworks, e.g., GDPR, PIA, etc., vi) enforce privacy by applying a Homomorphic encryption layer, through which all data interaction will take place, vii) a blockchain-based transaction recorder to ensure accountability. TRUSTEE's fully encrypted solution will be validated through six different use cases supporting GAIA-X, EOSC, EGI, etc. demonstrating a multi-disciplinary, Pan-European federated FAIR and private data ecosystem.

Artificial intelligence (AI) enables data-driven innovations in health care. AI systems, which process vast amounts of data quickly and in detail, show promise both as a tool for preventive health care and clinical decision-making. However, the distributed storage and limited access to health data form a barrier to innovation, as developing trustworthy AI systems requires large datasets for training and validation. Furthermore, the availability of anonymous datasets would increase the adoption of AI-powered tools by supporting health technology assessments and education. Secure, privacy compliant data utilization is key for unlocking the full potential of AI and data analytics. In this proposal, we will advance the current state-of-the-art data synthesis methods towards a more generalized approach of synthetic data generation. We will also develop metrics for testing and validation, as well as protocols that enable synthetic data generation without access to real-world data (through multi-party computation). We aim to provide: 1) Improved methods and technical pipelines for privacy-preserving data synthesis including different data formats such as EHRs and medical images, 2) Easy to use and configurable data services to enable AI developers’ access to larger pools of decentralized de-identified data through multi-party computing, 3) Provide anonymous data on demand or from a (temporary) repository, 4) Establish a Data Market – facilitating data sharing and monetization incl. incentives-based provision of data to the services, 5) Integrate the data market and the data service ecosystem as a X-European health data hub in the European Health Data Space, and 6) Validate the results with real-world use-cases focusing on high impact diseases, cancer types in particular.

RAIDO is a powerful framework solution designed to develop trustworthy and green artificial intelligence (AI). Trustworthy AI focuses on ensuring the reliability, safety, and unbiased optimization and deployment of AI systems, particularly in critical applications such as healthcare, farming, energy, and robotics. On the other hand, Green AI involves the development and deployment of energy-efficient and environmentally sustainable AI technologies, leading to reduced environmental impact and improved resource management. RAIDO provides an array of automated data curation and enrichment methods, including digital twins and diffusion models, to create high-quality, representative, unbiased, and compliant training data. It also offers various data- and compute-efficient models and tools to create energy-efficient Green AI, such as few- and zero-shot learning, dataset and model search, data and model distillation, and continual learning. To ensure the transparency, explainability, and reliability of the optimized AI models and data handling processes, RAIDO uses various XAI methods, decentralized blockchain, feedback-based reinforcement learning, novel KPIs, and visualization techniques. Additionally, the innovative AI orchestrator optimizes related tasks and processes, reducing the overall energy consumption and environmental footprint of the models during both development and deployment. RAIDO emphasizes the development of dynamic interfaces that support the appropriate AI paradigms (central, distributed, dynamic, hybrid) and enable seamless adaptation to the needs of the use situation. Furthermore, RAIDO will be evaluated through four real-life demonstrators in key application domains, such as smart grids, computer vision-based smart farming, healthcare, and robotics, showcasing notable societal and market impact.

TANGO will establish a stronger cross-sector data sharing, in a citizen-centric, secure and trustworthy manner, by developing innovative solutions while addressing environmental degradation and climate change challenges. The overall outcome is a novel platform exhibiting the following capabilities: user-friendly, secure, trustworthy, compliant, fair, transparent, accountable and environmentally sustainable data management, having at its core technology components for distributed, privacy preserving and environmentally sustainable data collection, processing, analysis, sharing and storage. This platform will promote trustworthy and digitally enabled interactions across society, for people as well as for businesses. TANGO will leverage the power of emerging digital technologies to strengthen the privacy for citizens and private/public organisations, reduce costs and improve productivity. It will unlock the innovation potential of digital technologies for decentralised, privacy-preserving applications, while making accessible and demonstrating this potential within the GAIA-X and EOSC ecosystem. With 37 key partners from 13 countries, TANGO, is uniquely positioned to provide a high impact solution within the transport, e-commerce, finance, public administration, tourism and industrial domains supporting numerous beneficiaries across Europe. Through the provision of TANGO technologies, a trustworthy environment will be designed acting as a gatekeeper to information and data flows. Citizens and public/private organisations will be empowered to act and interact providing data both online and offline. TANGO will focus its activities on 3 main pillars: (i) the deployment of trustworthy, accountable and privacy-preserving data-sharing technologies and platforms; (ii) the creation of data governance models and frameworks; (iii) the improvement of data availability, quality and interoperability – both in domain-specific settings and across sectors.