Secure Computing Continuum (IoT, Edge, Cloud, Data spaces)

Expected Outcome

Proposals are expected to contribute to one or more of the following:

• Enhanced resilience against distributed cyber threats and adversarial attacks targeting interconnected systems, while preserving data privacy in highly dynamic and heterogeneous environments;
• Security solutions tailored to distributed computing systems including for example IoT and edge computing environments, integrating privacy-preserving mechanisms;
• Improved interoperability of security measures and cross-layer security, including the compatibility of privacy-enhancing technologies across different layers of the computing continuum and data spaces, and across providers in the multi-provider edge cloud continuum.

Scope

This topic aims to advance security across the entire computing continuum, spanning IoT devices, edge computing, cloud infrastructures, AI computing environments, and data spaces. Proposals should address critical challenges such as ensuring data integrity in highly distributed and dynamic environments, implementing robust zero-trust architectures to secure interconnected and heterogeneous systems, and enabling comprehensive protection for sensitive data and processes. Privacy should be considered a core element of these approaches, ensuring that data confidentiality is preserved throughout its lifecycle, in compliance with relevant data protection frameworks, such as GDPR. Solutions are expected to deliver tangible and measurable security improvements across all layers of the continuum, prioritizing scalability, interoperability, trustworthiness, security and resilience against emerging threats.

Proposals are expected to address one or more of the following:

• Develop advanced security solutions for edge to cloud. For example, investigating lightweight cryptographic techniques, including in combination with or based on post-quantum cryptography, incorporated in zero-trust architectures, and decentralized security models to ensure end-to-end protection from edge to cloud, while maintaining privacy during data transmission and processing.
• Enhance interoperability of security measures across different computing layers. Exploring security protocols, identity and access management solutions, and cross-domain authentication mechanisms to seamlessly integrate security controls across diverse computing ecosystems, including privacy-preserving protocols that enable the secure and compliant data exchange.
• Develop portable, deployable PQC acceleration solutions through SW/HW secure co-design to secure user data and computing tasks across heterogeneous platforms and applications in all layers of the continuum.
• Improve resilience against distributed cyber threats. Exploring anomaly detection, including AI-driven anomaly detection, intrusion prevention techniques, and automated response mechanisms to counter emerging threats targeting interconnected infrastructures and data spaces, while upholding privacy through techniques such as federated analysis, secure multi-party computation, Fully Homomorphic Encryption or other.