A scalable and adaptable paradigm for implementing fine-grained authorization in distributed systems, such as smart healthcare, is Attribute-Based Access Control (ABAC). ABAC offers dynamic data sharing capabilities, which are crucial for modern healthcare systems, by allowing access decisions based on user attributes. ABAC is frequently combined with cryptographic techniques to further improve the security of data transfer and protect private medical records in untrusted settings. However, it is still difficult to ensure both effective management of massive amounts of medical data and robust security against new quantum threats. In this paper, we present a hybrid quantum-safe ABAC framework for secure smart healthcare data sharing. The proposed technique enables secure and efficient access control over encrypted medical records by combining fine-grained attribute-based policy enforcement with lightweight cryptographic primitives. The proposed scheme is appropriate for practical smart healthcare settings, as it supports efficient access verification and dynamic attribute management. Security analysis shows that the framework maintains data secrecy and access control correctness while offering resistance against quantum adversaries. Performance evaluation shows that the proposed framework achieves lower computational, communication, and storage overhead compared to existing approaches. Thus, the proposed framework integrates fine-grained attribute-based access control with quantum-safe communication techniques and blockchain-assisted attribute management in order to enable secure, efficient, and scalable data sharing in smart healthcare systems.
Vehicular consumer electronics increasingly support computation-intensive and latency-sensitive services, imposing stringent efficiency, reliability, and security requirements on vehicular edge computing (VEC) systems. In dynamic vehicular environments, inference-based information leakage and anomalous communication behaviors further threaten system performance and data privacy. To address these challenges, this paper proposes a UAV-assisted cooperative VEC architecture that integrates threat-aware task offloading with intelligent spatiotemporal caching across roadside units (RSUs) and UAV edge nodes. A security-aware uplink transmission model is developed to capture potential information leakage risks and abnormal communication patterns, enabling adaptive offloading decisions. We formulate a joint optimization problem to minimize end-to-end task execution delay while improving cache utilization under limited computing and storage resources. To efficiently solve this problem, a Threat-Aware Joint Optimization (TAGO) framework is designed by combining proximal policy optimization for adaptive task offloading and a gradient-based caching update derived from the Frank-Wolfe algorithm to capture spatiotemporal service popularity. Simulation results demonstrate that the proposed approach significantly reduces task delay and improves cache efficiency compared with several baseline strategies, showing its effectiveness for secure and efficient UAV-assisted vehicular consumer electronics systems.
A large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving is proposed and a surrounding agent centric data augmentation strategy is introduced to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training distribution without collecting additional raw data.
Shihao Zhang, Jing Yang, Ziyu Song et al.· IEEE Internet of Things Jour...· 1 citation
