The pervasive spread of disinformation across social media platforms has become a significant global challenge, disrupting democratic processes, undermining public trust, and fueling societal polarization. Existing approaches often neglect the dynamic and structural mechanisms that drive the spread and adoption of false narratives. This article leverages the well-established principles and methodologies of Statistical Mechanics and introduces a dynamic Mean-Field framework to model the evolution of disinformation within social networks. The framework introduces innovative elements, including heterogeneous coupling strengths to capture diverse social influences among network users, memory effects to account for cognitive inertia or belief re-evaluation and a three-state Potts model to represent polarization and neutrality in opinion dynamics. It employs the concept of effective fields to integrate external disinformation campaigns, facilitating a detailed analysis of critical thresholds and phase transitions. Monte Carlo simulations are performed to further illustrate the transient and equilibrium dynamics of belief adoption and rejection. Our findings provide actionable insights for the disinformation spread and offer a theoretical foundation for designing targeted interventions to mitigate its harmful effects on societies.

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

As cybercrime is becoming increasingly sophisticated, effective cybersecurity is crucial to safeguard digital assets and protect critical infrastructures from emerging threats. Several security applications exploit recent advances in (Big) data analysis and Artificial Intelligence (AI) to prevent and respond to malicious activities. Towards this direction, supervised and unsupervised Machine Learning (ML) methods are used to detect anomalies or reveal patterns that may indicate potential threats. However, the successful implementation of these technologies requires security practitioners to undergo specialized training to fully understand and use AI-driven tools and data analytics. On the other hand, AI models themselves are vulnerable to a variety of cyber threats, which can compromise their training data and learning processes. To ensure the safe operation of these systems, especially when deployed in adversarial environments, it is crucial to create novel AI adversarial algorithms and models that are resilient against diverse security threats. This work presents a conceptual framework based on Large Language Models (LLMs) supported by a Multi-Agent layer for training of security practitioners in various advanced technologies and enhance ML models ability to detect and respond to emerging cyber threats effectively.