The integration of vehicular networks with cloud and IoT infrastructures requires secure and lightweight authentication to ensure privacy and reliability in highly dynamic environments. This paper proposes an adaptive multi-factor authentication protocol that combines biometrics, passwords, and elliptic curve cryptography (ECC) to provide strong mutual authentication between vehicles, roadside units (RSUs), and cloud servers. A Reed–Solomon–based fuzzy extractor is incorporated to address biometric variability, ensuring stable key generation without compromising efficiency. The protocol achieves resilience against impersonation, replay, stolen credential, and man-inthe-middle attacks, supported by a formal proof under the Random Oracle Model (ROM). To validate practicality, the scheme is implemented in Python and evaluated in a realistic vehicular simulation environment using OMNeT++, SUMO, and Veins with real-world road network data. Experimental results demonstrate low authentication delay, reduced communication and computational overhead, and high scalability under varying vehicle densities, confirming its suitability for secure and realtime vehicular cloud environments.

Preserving an individual privacy in the Artificial Intelligence(AI)era is a pressing challenge, as traditional anonymization techniques often fall short againstAI-driven threats. WhileAIhas enabled transformative advancements across sectors such as healthcare, finance, aviation, smart cities, and the Internet of Things (IoT), it also introduces new privacy challenges. In particular,AI-driven background knowledge attacks occur when adversaries combine auxiliary information from external sources such as public records, social media, or previously leaked datasets with advancedAItechniques to re-identify individuals or infer sensitive details from anonymized datasets, rendering conventional defenses less effective. In this study, we presentPrivacyGuard, an automated framework that integrates machine learning with traditional anonymization to address these risks.PrivacyGuardemploys K-Nearest Neighbor and Random Forest classifiers to identify and evaluate privacy risk of each data tuple inAI-driven attack scenarios. Following risk assessment, Laplace-based differential privacy is applied to mitigate identified risks, while fuzzy logic forms equivalence groups that reflect varying risk levels. Experimental validation on the 1:M Adult dataset (an individual having multiple records) demonstrates thatPrivacyGuardachieves 96.47% accuracy and mitigates approximately 42.21% of the identified risks, confirming its effectiveness in balancing privacy and data utility against emergingAI-driven threats.

Recent developments in the Internet of Vehicles (IoV) and vehicular adhoc networks (VANET) have revolutionized our infrastructure, making it safer, more convenient, and efficient. VANET provide smart traffic control, event allocation, and real-time information. Existing vehicles in VANET are now equipped with intelligent navigation, entertainment, and emergency applications. However, the highly connected nature of these vehicles poses a significant safety and security risk to drivers and assets which can result in life-threatening consequences. Location privacy is critical, and robust network security techniques should be used to counter threats in VANET environments. Existing schemes like obfuscation, mix-zones, and silent periods have preserved location privacy to some extent but have poor Quality of Service (QoS) and lack both efficiency and security. To address these issues, a shadowing scheme is introduced, which is an improvement of earlier schemes used for location privacy. This approach ensures better service to the vehicle by allowing precise location-based service (LBS) requests to the LBS server and uses blockchain technology for storing vehicular certificates. The inclusion of a group leader significantly reduces the time taken for implementing the scheme, improving efficiency and scalability. The anonymity set size increases over time, offering better privacy protection especially in densely populated areas. The proposed scheme overcomes drawbacks of existing techniques which includes reduced location accuracy and low-quality service in spatial obfuscation techniques, limited applicability and high tracking rate in shadow-based approaches, and reduced utility in distance-based schemes. Moreover, single point of failure and resource-intensive group formation in group-based schemes, and dependency on additional infrastructure in mix-zone-based schemes are also overcome. The proposed scheme’s experimental results validate it, showing that it outperforms current state-of-the-art schemes based on metrics, such as anonymity set size, entropy, and tracking success ratio.

Containers have gained immense popularity as a portable and lightweight virtualization solution. They facilitate application development, deployment and distribution across computing environments. Their success is also attributed to the support they offer for DevOps teams and for applications developed using a microservices architecture style. Containers are not the only components in the environment but work closely with other components for managing and supporting them, forming an ecosystem. Architectural modeling can be used as a powerful tool to represent ecosystems which helps understand, build and secure such complex systems. We describe in this paper several models we have created for container ecosystem components. These models are abstract, and they help generalize the systems to handle complexity and heterogeneity; they provide a common vocabulary and build holistic and unified views of the systems. The use of UML for modeling improves precision. This can lead to better implementations with respect to reliability, security and interoperability compared to ad hoc methods. A reference architecture will not just facilitate the work of developers and security engineers but also of anyone who aims to ensure compliance, privacy, safety, reliability and/or governance for container ecosystems and we show how to build one. We also describe relationships between container, cloud and IoT ecosystems. This paper is part of our work on developing a security reference architecture for container ecosystems.