Mobile edge computing (MEC) is a promising paradigm that provides abundant computation and storage resources at the edge close to mobile devices (MDs). In MEC networks, MDs offload compute-heavy tasks to nearby edge servers (ESs) for delay-sensitive processing, where relevant services are stored to support task execution. However, the limited computation and storage capacities of ESs make joint optimization of service caching and computation offloading challenging due to coupled decisions, a large solution space, and dynamic environments. In this paper, we investigate the joint optimization of service caching and computation offloading in MEC networks, aiming to maximize the cache hit ratio and minimize the average service latency. To tackle this problem, the original formulation is decomposed into two hierarchical subproblems, namely high-level service caching and low-level computation offloading. We propose a novel hierarchical deep reinforcement learning (DRL) algorithm with active inference, termed HADRL. At the high-level, we adopt a deep deterministic policy gradient (DDPG) based DRL approach to maximize the cache hit ratio. At the low-level, we employ an active inference based DRL approach to minimize the average service latency. Unlike conventional DRL, the active inference based DRL approach selects policies by minimizing expected free energy instead of relying only on explicit rewards, making it well suited for highly dynamic low-level computation offloading. According to the simulation outcomes, the HADRL scheme surpasses the benchmark algorithms with respect to cache hit ratio as well as average service latency.

Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching (tolerance to typos and variants in query keywords), dynamic updates, and result verification, designing a service that supports Dynamic Verifiable Fuzzy Search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presentsVeriFuzzy, a novel DVFS service framework that cohesively integrates three innovations: anEnhanced Virtual Binary Tree (EVBTree)that decouples fuzzy semantics from index logic to support$O(\log n)$search/updates; ablockchain-reconstructed verificationmechanism that ensures result integrity with logarithmic complexity; and adual-repository state managementscheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41% faster search,$5\times$more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.

As healthcare systems evolve and healthcare data grows, the need for cross-domain collaboration treatment has become more complex, necessitating fine-grained access control to enhance privacy and security. Blockchain provides a distributed trusted platform without third parties, but the current blockchain-based access control systems lack efficiency and sufficient privacy protection in cross-domain collaboration. To address these challenges, we propose SWIFTGUARD, an efficient and fine-grained access control system based on a master-slave chain to strengthen the security and privacy of cross-domain healthcare collaboration. SWIFTGUARD incorporates a zero-knowledge proof protocol for cross-domain authentication with-out exposing sensitive data and leverages quantitative attribute weights for efficient access control. Through game-based security proof, we demonstrate the zero knowledge and soundness of the system. Extensive experiments evaluate that SWIFTGUARD reduces the time complexity of access authorization from O($n$) to O(log$n$), with improved throughput and stable performance in cross-domain collaboration. Our comprehensive evaluation confirms that SWIFTGUARD provides a secure and efficient access control system for cross-domain healthcare collaboration.