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RESCALE aims at designing, building, and demonstrating secure-by-design supply chains. To this end, RESCALE will (i) automate the evaluation processes of both software and hardware components, (ii) ensure that third-party segments are free from vulnerabilities, (iii) offer effective audit procedures for cybersecurity testing, and (iv) enable the construction of secure systems with the strongest possible guarantees.


In an age of digital information and communication, cybersecurity threats cannot be avoided even in the healthcare industry. In fact, healthcare information infrastructures (HCIIs) are targets due to their increasing digital interconnectivity. As such, there is an urgent need for health service operators to protect HCIIs. The EU-funded AI4HEALTHSEC project will develop a solution that improves the detection and analysis of cyberattacks and threats on HCIIs. The aim is to build situational awareness and incident handling and risk assessment among HCIIs.


The FLUTE project is set to revolutionize healthcare data utilization through a privacy-preserving approach. Our project aims to improve predictions of aggressive prostate cancer through AI support to physician, while minimizing unnecessary biopsies, ultimately benefiting patients and reducing associated costs.


Project Background

Even though the number of terrorist attacks have remained approximately at the same level as the previous years (119 in 2020 and in 2019, 129 in 2018), there is a new factor that plays a pivotal role for the spread of violent extremism propaganda – the Internet. One of the most dangerous and trending types of misuse of the opportunities that the Internet offers in the field of public security has been proven to be the usage of social media and websites for dissemination of terrorist content.


The use of the Internet of things (IoT) is constantly expanding. However, cyber-attacks, ransomware, and security incidents are also on the rise. These incidents are currently not addressed by cybersecurity practices. The implementation of new technologies could raise the frequency and complexity of such incidents. This poses a danger to IoT users worldwide.


Security of open-source solutions in the business interconnected market (especially in IoT where a single product may include components from various Tier 1 or OEM manufacturers) is hard to assure. OEM SW/HW developers that employ open-source solutions must assume that any component provided by 3rd parties needs to be reassessed for security as there is not holistic security auditing/testing process to cover the full production line.


The SENTINEL project will deliver a solution that will enable a novel “one-stop shop” approach to integrated and obtainable private and personal data protection compliance for SMEs/MEs.

This will be achieved through the adherence to the following project propositions:


The EU-funded ORSHIN project aims to build connected OSH devices, such as (I)IoT ones, taking advantage of unprecedented opportunities provided by open-source hardware. The project will specify a novel and dependable methodology to develop, maintain and decommission OSH devices which we call trusted life cycle. The project will research new formal verification models and tools to protect OSH devices from critical threats such as side-channel and fault injection vulnerabilities.


TEADAL will enable the creation of trusted, verifiable, and energy-efficient data flows, both inside a data lake and across federated data lakes, based on a shared approach for defining, enforcing, and tracking data governance requirements with specific emphasis on privacy/confidentiality. The proposed stretched data lake, i.e., deployed in the continuum, will be based on an innovative control plane able to exploit all the controlled/owned resources, across clouds and at the edge, to improve data analysis.


STELAR will design, develop, evaluate, and showcase an innovative Knowledge Lake Management System (KLMS) to support and facilitate a holistic approach for FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready (high-quality, reliably labeled) data.