WeldGalaxy project will deliver, a B2B online Platform that brings together global buyers (end-users/OEM) and EU sellers (manufacturers/suppliers/distributors/service providers) of welding equipment along with auxiliaries/consumables and services, thereby enhancing the visibility of EU’s welding products/prototypes/services to global users (via digital marketing strategies) and providing innovative web-based services (e.g. equipment selection and inventory management, digital design/testing of equipment capabilities) to boost EU market share and competitiveness. The digital platform will incorporate Knowledge base engineering (KBE) tool that streamlines equipment selection process for end-users and allows ‘plug and produce’ digital manufacturing of the right equipment to specified customers’/end-users’ requirements and regulatory compliance. Though the full capability of the WeldGalaxy platform including associated product services (including the services from all third parties) will be demonstrated in welding equipment (along with auxiliaries) and consumables manufacturing domain, yet, the conceptual and functional framework of WeldGalaxy technology concept can be used in any industrial domain related to manufacturing. The Dynamic Knowledge Management based B2B platform will be designed by following the standard 3-tier architecture. Scalability and reliability will be assured by the use of: RESTfull architecture for API layer, cloud-based backend platform hosted on mainstream cloud providers like AWS or Google Cloud Platform who offer clustering, loading balancing, caching to support scalability and redundant data backup to ensure reliability. Use of blockchain/Distributed Ledger Technology (DLT) will make the platform inherently stable, highly scalable and always up. The digital platform, supported by integrated blockchain/DLT for improved reliability/visibility/ transparency/ security of transactions, will enhance the competitiveness of EU manufacturing sec

The goal of the OPERANDO project is to specify, implement, field-test, validate and exploit an innovative privacy enforcement platform that will enable the Privacy as a Service (PaS) business paradigm and the market for online privacy services. The OPERANDO project will integrate and extend the state of the art to create a platform that will used by independent Privacy Service Providers (PSPs) to provide comprehensive user privacy enforcement in the form of a dedicated online service, called “Privacy Authority”. The OPERANDO platform will support flexible and viable business models, including targeting of individual market segments such as public administration, social networks and Internet of Things. A key aspect addressed by OPERANDO is the need to simplify privacy for end users (data subjects). OPERANDO will support a simple Privacy Dashboard allowing users to specify their preferences. These will be automatically compared with Online Service Provider (OSP) privacy policies and translated into personal data access control decisions by the PSP. OPERANDO will also address OSP requirements for simplified privacy compliance checking and auditing, to verify that they will meet user expectations or to satisfy privacy regulators. The technology will be trialled in the health care and public administration sectors. The OPERANDO consortium thereby aims to contribute to the entire ecosystem of online privacy stakeholders: Users, PSPs, Online Service Providers and Regulators. Federation of Privacy Authorities will be supported to increase value of the services and their uptake. The OPERANDO platform will be positioned for endorsement by European governments and standardization bodies. To increase transparency of the privacy services and dissemination of results, OPERANDO outcomes will be implemented in Open Source, and will be made available to the community for evolution and value-adding beyond the scope of the project.

The PharmaLedger project will create a blockchain-based framework for the efficient digitization of the healthcare industry. The goal of the project is to provide a widely trusted platform that will support the design and adoption of blockchain-enabled healthcare solutions while accelerating delivery of innovation that will benefit the entire ecosystem, from manufacturers to patients. PharmaLedger will serve as a single source of truth for the healthcare ecosystem and will be designed for efficient decentralized governance, wide adoption by the stakeholders of the ecosystem, compliance with extant and emerging standards and regulation, and end-to-end connectivity and interoperability. Sustainability of the platform will be ensured by leveraging existing, successful blockchain technologies; open source reference implementation; and a fully documented, actionable methodology for evolutionary digitization of the healthcare industry. The project will address the key challenges of the healthcare ecosystem through prioritized delivery of applications and validation of business use cases, including but not be limited to end-to-end product tracking for combating counterfeit medicines and medical supplies; supply chain integrity; efficiency of recruitment and submission in clinical trials; and machine-learning health data marketplaces. The platform will support integrated use of medical devices across the use cases. The PharmaLedger project brings together 28 partners from 10 EU Member States, including 11 large pharmaceutical companies; highly innovative technology SMEs specializing in blockchain development, security, privacy and business intelligence; universities and research institutes specializing in pharmacoeconomic analysis, research of patient requirements, big data analytics and electronic health records; leading clinical trials companies; supply chain partners; patient representatives; and leading healthcare service providers.

The FLUTE project will advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments. The technical innovations will be integrated in a privacy-enforcing platform that will provide innovators with a provenly secure environment for federated healthcare AI solution development, testing and deployment, including the integration of real world health data from the data hubs and the generation and utilization of synthetic data. To maximize the impact, adoption and replicability of the results, the project will contribute to the global HL7 FHIR standard development, and create novel guidelines for GDPR-compliant cross-border Federated Learning in healthcare. To demonstrate the practical use and impact of the results, the project will integrate the FLUTE platform with health data hubs located in three different countries, use their data to develop a novel federated AI toolset for diagnosis of clinically significant prostate cancer and perform a multi-national clinical validation of its efficacy, which will help to improve predictions of aggressive prostate cancer while avoiding unnecessary biopsies, thus improving the welfare of patients and significantly reducing the associated costs. Team. The 11-strong consortium will include three clinical / data partners from three different countries, three technology SMEs, three technology research partners, a legal/ethics partner and a standards organization. Collaboration. In accordance with the priorities set by the European Commission, the project will target collaboration, cross-fertilization and synergies with related national and international European projects.

In recent years, Federated Learning (FL) has emerged as a revolutionary privacy-enhancing technology and, consequently, has quickly expanded to other applications. However, further research has cast a shadow of doubt on the strength of privacy protection provided by FL. Potential vulnerabilities and threats pointed out by researchers included a curious aggregator threat; susceptibility to man-in-the-middle and insider attacks that disrupt the convergence of global and local models or cause convergence to fake minima; and, most importantly, inference attacks that aim to re-identify data subjects from FL’s AI model parameter updates. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross border European datasets with privacy guarantees that exceed the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients’ privacy. The strong privacy protection accorded by the platform will be verified through the engagement of external experts for independent privacy leakage and re-identification testing. A secondary goal is to research, develop and promote with EU data protection authorities a novel metric and tool for the certification of GDPR compliance of FL implementations. The consortium is composed of 9 interdisciplinary partners: 3 Research Organizations, 1 University, 3 SMEs and 2 Clinical partners with extensive experience and expertise to guarantee the correct performance of the activities and the achievement of the results.