The objective of EuWireless proposal is to develop the design of the first pan-European infrastructure to support research in mobile communication networks using regulated spectrum, with the goal of contributing to keep Europe’s leadership in mobile communication technologies. The output of the project will be a complete design report addressing the pan-European operator, the regulatory aspects, the business model, the technical solutions and the roadmap for implementation of the proposed infrastructure, as well as prototypes that confirm its feasibility.

In recent years, robots are being increasingly deployed outside strictly controlled environments. When faced with unexpected situations, these robots are often incapable of taking appropriate action and require human intervention. The goal of CONVINCE is to advance the capabilities of robots to perform complex tasks robustly and safely within unstructured environments via autonomous and unsupervised adaptation to the environment and operational context. More specifically, the key contribution is to develop cognitive deliberation capabilities that ensure safe robot operation over extended periods of time without human intervention. These capabilities will be integrated into a model-driven software toolchain to allow developers to build application-specific deliberation systems able to i) determine robot’s behaviors required to fulfill a given task, also taking into account the context in which the robot operates and the experience gained during previous executions of the same task, ii) deploy and configure the components that are required to execute these behaviors, iii) automate the analysis of behaviors to ensure that they are safe and secure, leveraging on formal models and tools for design-time and run-time verification. The toolchain shall be based on proven system modeling concepts, particularly from the EU-funded project RobMoSys. Major parts shall be open-sourced with adapters to relevant robotics frameworks like the Robot Operating System (ROS). To ensure real-world applicability, CONVINCE will demonstrate the technology developed in the project on three different real-world use cases, each of which presents unique technical difficulties and utilizes robotic systems of increasing complexity, in different application domains: vacuum cleaner robot, assembly robot, and robotic museum guide.

Cybercrime has recently shifted from attacking big corporations to smaller industries, like financial services as well as the healthcare sector. Especially in the last area the trend is rising, where hackers are targeting patient health devices that are connected to the internet. Most cases include stealing patient information and encrypting it for ransom money. The big problem is interconnection, each application or device that runs on the networks represents a possible entry point for a cyber-physical attack. So far, most hackers infected hospital software with ransomware to prevent staff from accessing patient records or scheduling appointments. But capable terrorists would also be able, to render active medical devices not just useless, but deadly. Complete cybersecurity in the health sector is unachievable, and would exceed financial means; nevertheless, vital steps can be taken to minimize the risk of cyber- attacks against healthcare facilities. Around 85 percent of targeted cyber-attacks would be preventable if basic protection protocols would be established. The SecureHospitals.eu project seeks to raise awareness on risks and protection opportunities, setup training schemes and the initiate training sessions for IT staff working in hospitals. Through several training approaches, the project will boost the level of training in cybersecurity in Europe, improve the knowledge of staff and in turn contribute to decreased vulnerabilities against cyberthreats and increased patient trust and safety.

The overall objective of TrustChain is to create a portfolio of Next Generation Internet protocols and an ecosystem of decentralised software solutions that reach the highest standards of humanity such as those chartered by the United Nations including the respect of human rights, ethics, sustainability, energy efficiency, our care for the environment and our respect for the World’s cultural history. TrustChain will tackle several challenges pertaining to trustworthy and reliable digital identity, to resilient, secure and reliable data pathways, to economics and trading of data, to energy efficiency for data storage, transport and sharing, to seamless services and data flows. A new trustworthy data governance and sharing model in line with the European regulatory framework and taking into account European values will be developed that will ensure Trusted Data Ecosystems. Third party top Internet innovators will be selected through 5 open calls focus on topics of the (1) Decentralised digital identity, (2) the User privacy and data governance, (3) Economics and democracy, (4) Multi chains support for NGI protocols, and (5) Green scalable and sustainable DLTs, which is an essential need of the day. TrustChain addresses the second research area of the Human 01-03 call in particular to tackle the current limitations of decentralised technologies, such as Blockchain and DLT, including those related to scalability, interoperability, energy efficiency, privacy or security, in order to make them dependable building blocks of the future Internet. TrustChain will explore DLT-based solutions, enabling the exploitation of data coming from a high number and various types of sources, eliminating data silos through decentralised and interoperable approaches, while helping individuals and organisations better govern their data when they participate in joint value chains where cooperating partners can also be competitors.

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.