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QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute
Journal article   Peer reviewed

QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute

Jie Zhang, Xiaohong Li, Mengke Zhang, Ruitao Feng, Shanshan Xu, Zhe Hou and Guangdong Bai
IEEE Internet of Things Journal, Vol.Early Access, pp.1-17
22/05/2026
DOI: https://doi.org/10.1109/JIOT.2026.3695861
Appears in  Recent Faculty of Science and Engineering Publications
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Abstract

Anonymity Quantification Attribute-Based Access Control Blockchain Entropy-Weighted Path Tree Privacy Preservation Authorization Access control Modeling Optimization Privacy Internet of Things Algorithms Security Educational institutions Modules (abstract algebra)
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.

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