European AI Security Network (EASiNet)

EASiNet brings together several European funded projects to collaborate on AI and cybersecurity
in different fields. EASiNet aims to raise awareness, exchange project results, promote open science,
and develop common strategies for project exploitation. EASiNet invites collaborations to work together and address critical issues in the intersection of AI and cybersecurity.

Member of EASiNet include the following:


AI and ML for the good of EU research

European researchers, innovators, companies, and citizens have a place to publish, find and reuse data, tools and services for research, innovation, and educational purposes. This is the European Open Science Cloud (EOSC). This environment will offer researchers across the EU an unprecedented mix and quality of tools for use during their research and testing, as well as the ability to better communicate with research fellows. The EU-funded AI4EOSC project will further increase this toolset by developing an array of artificial intelligence (AI), machine learning (ML) and deep learning models, bundled for ease of use to researchers. These models will be customisable so users can adapt them to their needs.


Offering IoT stakeholders a high level of security

The Internet of Things (IoT) provides numerous business opportunities. However, it also comes with new challenges such as compromised security, heightening the importance of IoT infrastructure security management. In line with this, the EU-funded CERTIFY project will define a methodological, technological and organisational approach towards IoT security life cycle management. The project will also design and implement a cybersecurity life cycle management framework for IoT devices that will operate by gathering and sharing information both internally and externally. Project work will pave the way for innovative privacy and security approaches in a broad spectrum of IoT environments.


Privacy-preserving framework for cross-border data processing

The Big Data revolution has created many new opportunities for research and industry. However, privacy concerns remain, particularly when processing Big Data. With this in mind, the EU-funded ENCRYPT project will address the challenges. It will develop a scalable framework for processing data stored in federated cross-border data spaces. ENCRYPT framework will provide a recommendation engine for end users to personalise their privacy preferences based on the sensitivity of their data and the trade-off between security and performance. In-lab tests and real-world use cases across various sectors (health, cybersecurity and finance) will validate the framework. The project is led by a consortium of 14 partners, including companies, research institutes and universities.


Pioneering secure healthcare AI across borders

Healthcare data holds the key to groundbreaking discoveries, but privacy concerns have been a roadblock. Harnessing the potential of data-driven healthcare while preserving privacy and security is a challenge. The EU-funded FLUTE project addresses this by pioneering novel methods for cross-border data utilisation. FLUTE focuses on enhancing secure multi-party computation in federated learning, with advanced artificial intelligence (AI) models and secure execution environments. These innovations will form a privacy-focused platform for healthcare AI solutions development. FLUTE will integrate with health-data hubs across three countries, creating a powerful AI toolset for prostate cancer diagnosis. This multinational effort aims to improve predictions, reducing unnecessary procedures and cutting costs.


Data analytics and cryptography for privacy preservation

To address customers’ needs, organisations rely on large volumes of user data combined with tailored statistical analysis to adapt their services accordingly. Machine learning models are being applied in applications. However, such service improvements and personalisation based on user data analysis increases the risk of privacy loss. Moreover, systems using such models incorporate often inexact, biased, and unfair proxies. The EU-funded HARPOCRATES project will lay the foundation for digitally blind evaluation systems designed to eliminate proxies. The project plans to design several practical cryptographic schemes (functional encryption and hybrid homomorphic encryption) for analysing data in a way that preserves privacy and enables a comprehensive approach where data analytics and cryptography are associated with increased privacy.


Bringing artifical intelligence to everyday healthcare

Artificial intelligence (AI) offers vast potential for the future of personalised medicine. Promising tailor-made treatments for patients, AI could help win the fight against serious illnesses such as cancer. However, the introduction of AI-enabled personalised medicine also presents challenges. Chief among these is the translation of AI-based suggestions into practical decision-making processes and treatment strategies. The EU-funded KATY project will develop an AI-empowered personalised medicine system that will greatly assist medical professionals and researchers in utilising and interpreting AI data in their daily work. This next-generation technology will bridge the gap between AI data and medical application and thus become a powerful tool in diagnosing, treating and defeating serious illnesses.


Assessing cancer therapies in real time

Real-world data (RWD) on patient health status are collected from various sources, such as electronic health records and wearables. Harnessing the power of RWD is challenging but offers the possibility to unlock actionable healthcare insights. The key objective of the EU-funded ONCOVALUE project is to collect and analyse RWD from European cancer hospitals and institutes as a means of assessing the effectiveness of novel cancer therapies. The consortium will use AI technologies to transform unstructured data from medical notes and images into real-world evidence made available to clinicians for treatment decision-making, and to regulatory bodies for developing guidelines. Collectively, ONCOVALUE will contribute to safer and more efficient therapies and technologies.


Safe and efficient machine learning platform for medical applications

While machine learning (ML) can lead to great advancements with respect to digital services and applications in the field of medicine, the training process based on real medical patient data is blocked by the fact that uncontrolled access to and exposure of such assets is not allowed by data protection legislation. The EU-funded PAROMA-MED project aims to develop novel technologies, tools, services and architectures for patients, health professionals, data scientists and health domain businesses so that they will be able to interact in the context of data and ML federations according to legal constraints and with complete respect to data owners rights from privacy protection to fine grained governance, without performance and functionality penalties of ML/AI workflows and applications.


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


Empowering citizens and patients to take control of their health

It is believed that early risk prediction through the use of artificial intelligence can empower citizens to adopt healthier habits and a better lifestyle. The EU-funded WARIFA project aims to define a personalised early risk prediction model that will be used to support individual preventive measures as well as early intervention. The proposed technology can help empower both citizens and patients. The digital tool will mainly focus on three scenarios: the fight against skin cancer, the late complications of diabetes mellitus and the main lifestyle risk factors involved in noncommunicable diseases.