Our Projects
Innovation and Research at European Level
EQUIVOR
EQUIVOR (Equivocation-Resistant Consensus for Distributed Ledger Technologies) introduces a lightweight, energy-efficient consensus protocol tailored for sharded blockchain ecosystems. To address the vulnerabilities inherent in scalable high-throughput networks, the project deploys the DERC (Dynamic Equivocation-Resistant Consensus) protocol. By fusing advanced state analysis, cryptographic voting mechanisms, and AI-driven probabilistic detection, EQUIVOR proactively secures the network against malicious behavior. Aligned with TrustChain’s vision, the solution delivers high reliability and scalability while reducing consensus-related energy consumption by up to 30% compared to legacy protocols.
Period: 2025 - 2026
QUAS
QUAS is a framework designed to future-proof modern communication standards against emerging quantum threats by embedding Post-Quantum Cryptography (PQC) into QUIC and SIP protocols. Addressing the vulnerabilities of legacy encryption, the project replaces traditional key exchanges and signatures with NIST-recommended quantum-safe alternatives, such as Kyber, Dilithium, and HQC. By integrating these algorithms directly into real-time transport and authentication flows for Web (HTTP/3) and VoIP services, QUAS moves beyond theoretical testing to deliver practical, deployment-ready security. Closely aligned with the PQ-REACT vision for cryptographic agility, the solution employs hybrid flows to ensure backward compatibility, enabling the early adoption of robust defense mechanisms across critical digital infrastructure.
Period: 2025 - 2026
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SHASAI
SHASAI (Secure Hardware and Software for AI systems) targets the critical intersection of hardware/software security and high-risk AI systems, aiming to enhance their resilience, automated testing, and continuous assessment. Adopting a comprehensive lifecycle approach, the project delivers advanced risk assessment methodologies, secure design patterns, and tools to safeguard the supply chain against dependency vulnerabilities, poisoned data, and model backdoors. Furthermore, SHASAI provides a virtual testing platform for automated defense strategies and AI-driven monitoring services to ensure integrity during operation. Validated through real-world scenarios in the agrifood, healthcare, and automotive sectors, the project ensures strict alignment with key regulations such as the EU AI Act and the Cyber Resilience Act, fostering the development of trustworthy AI ecosystems.
Period: 2025 - 2029
REDUCE
REDUCE (Reduction through Engagement and Data Utilization for Controlling Excess) represents a strategic fusion of active user engagement and state-of-the-art AI designed to tackle the critical issue of plate waste in canteen environments. By aggregating multi-dimensional data, ranging from waste volumes and environmental metrics (such as temperature and humidity) to consumer behavioral patterns, the solution establishes a comprehensive foundation for machine learning. Advanced AI algorithms leverage this dataset to forecast waste generation under varying conditions. These predictive insights translate into actionable intelligence for catering operators, enabling dynamic adjustments to menus, portion sizing, and service delivery. Ultimately, REDUCE empowers organizations to transition from reactive measures to proactive waste management, significantly enhancing sustainability and operational efficiency.
Period: 2025
