Due to its flexibility in terms of charging and billing, the smart grid is an enabler of many innovative energy consumption scenarios. One such example is when a landlord rents their property for a specific period to tenants. Then the electricity bill could be redirected from the landlord’s utility to the tenant’s utility. This novel scenario of the smart grid ecosystem, defined in this paper as Grid-to-Go (G2Go), promotes a green economy and can drive rent reductions. However, it also creates critical privacy issues, since utilities may be able to track the tenant’s activities. This paper presents P4G2Go, a novel privacy-preserving scheme that provides strong security and privacy assertions for roaming consumers against honest but curious entities of the smart grid. At the heart of P4G2Go lies the Idemix cryptographic protocol suite, which utilizes anonymous credentials and provides unlinkability of the consumer activities. Our scheme is complemented by the MASKER protocol, used to protect the consumption readings, and the FIDO2 protocol for strong and passwordless authentication. We have implemented the main components of P4G2Go, to quantitatively assess its performance. Finally, we reason about its security and privacy properties, proving that P4G2Go achieves to fulfill the relevant objectives.

This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. The work emphasizes Continuous Integration/Continuous Deployment (CI/CD) practices, development, and versatile integration infrastructures. The described methodology ensures efficient, reliable, and seamless integration of the framework, offering valuable insights into integration design, data flow, and the incorporation of cutting-edge technologies. Through three real-world trials in smart cities, e-health, and smart manufacturing and the development of a comprehensive QuickStart Guide for deployment, this work highlights the efficiency and adaptability of the COGNIFOG platform, presenting a robust solution for addressing the complexities of next-generation computing environments.

Large Language Models (LLMs) have recently attracted considerable attention from the scientific community, due to their advanced capabilities and potential to serve as vital tools across various industries and academic fields. An important implementation domain for LLMs is Data Science, in which they could enhance the efficiency of Data Analysis and Profiling tasks.With the utilization of LLMs in Data Analytics tools, end-users could directly issue data analysis queries in natural language, bypassing the need for specialized user interfaces. However, due to the sensitive nature of certain data in some organizations, it is unwise to consider using established, cloud-based LLMs. This article explores the feasibility and effectiveness of a standalone, offline LLM in generating code for performing data analytics, given a set of natural language queries. A methodology tailored to a code-specific LLM is presented, evaluating its performance in generating Python Spark code and successfully producing the desired result. The model is assessed on its efficiency and ability to handle natural language queries of varying complexity, exploring the potential for wider adoption of offline LLMs in future data analysis frameworks and software solutions.

In the face of increasingly sophisticated and dynamic cyber threats, policy-based cybersecurity deployment has emerged as a critical strategy for securing modern, complex network infrastructures. This approach enables organizations to enforce adaptable, scalable and context-aware security policies that can dynamically respond to evolving threats and operational demands. However, the growing complexity of distributed systems and the interdependencies between diverse security components present significant challenges in ensuring consistent and efficient policy enforcement. Static and centralized security models are no longer sufficient to address these challenges, underscoring the need for intelligent, distributed solutions that can optimize policy deployment in real-time. This paper introduces a novel framework based on Message Passing algorithms that enables decentralized, adapted and optimized policy deployment across complex cybersecurity infrastructures. More specifically, we extend the traditional Min-Sum algorithm by incorporating dynamic trust weights that influence policy enforcement decisions, allowing more reliable nodes to have a greater impact while mitigating the risks posed by untrusted entities. The proposed framework is designed to handle large-scale network environments with minimal computational overhead, relying on localized message exchanges instead of centralized computations. We benchmark our framework against state-of-the-art approaches, demonstrating that the proposed framework achieves lower total enforcement cost while significantly improving convergence speed, particularly in heterogeneous trust environments. Finally, we provide an in-depth evaluation of how trust heterogeneity influences security policy enforcement. Extensive simulations were conducted to evaluate the framework’s effectiveness in optimizing policy deployment for varying network topologies, demonstrating significant improvements in threat detection accuracy, policy consistency and resource efficiency across diverse and dynamic network environments.

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.