Quantum key distribution (QKD) offers unconditional security for mobile aerial networks, but existing protocols face challenges in low-altitude, continuously moving aerial networks because of hardware complexity and sensitivity to channel noise. This paper proposes mobile aerial semi-quantum communication (MASQC), a novel lightweight protocol that integrates and adapts decoy state analysis with time-batched polarization encoding over free-space optics to enable secure key distribution among aerial vehicles (AVs) and base stations. MASQC introduces six key innovations tailored for aerial networks: (1) a decoy state semi-quantum protocol implementation requiring only passive single-photon detection on AVs while providing robust security against photon-number-splitting attacks, (2) time-batched polarization encoding with 1-5 ms windows and real-time trajectory prediction to compensate for Doppler effects, (3) comprehensive post-quantum authentication infrastructure using CRYSTALS-Dilithium signatures and CRYSTALS-Kyber key encapsulation, (4) adaptive security thresholding (0.08-0.12) with dual-layer error analysis combining conventional QBER and decoy state bounds, (5) hybrid quantum-classical resilience with emergency key pools and perfect forward secrecy mechanisms, and (6) encrypted relay capabilities through neighboring AVs during line-of-sight disruptions. Unlike prior semi-quantum or decoy-state protocols designed for static environments, MASQC provides a holistic system solution addressing the unique constraints of low-altitude aerial networks. Comprehensive simulation results using the Qiskit framework demonstrate the effectiveness of MASQC with an 85.3% network success rate, high bits/second key generation rate per AV, and robust security validation including a high attack detection probability against sophisticated eavesdropping attempts.

The core challenge of steganalysislies in detecting extremely weak signals. While deeper networks improve accuracy, they exacerbate the risks of overfitting and poor generalization. The root cause is attention misalignment, where models inherently focus on semantic content rather than the texture rich regions containing steganographic signals. To address this, we propose a general and lightweight Attention Misalignment Correction (AttMC) mechanism. Leveraging the natural tendency of filter detection to focus on textural regions, our mechanism guides the model's attention via a knowledge-sharing strategy. Specifically, we design an auxiliary task of random block-wise filter detection to explicitly teach the model to prioritize textural areas. This texture-centric attention is then transferred to the main task through a shared network backbone. This process not only corrects the model's attention misalignment but also steers the decision boundary toward a more robust and generalizable solution. Extensive experiments demonstrate that our plug-and play AttMC mechanism significantly improves the detection accuracy and generalization performance of existing models with a negligible increase in parameters.

Internet of Medical Things (IoMT) aggregates a series of smart medical devices and fully uses the collected health data to improve user experience, medical resource utilization, and full life cycle protection. However, privacy leakage, data loss, and inefficient sharing problems are still serious in the data-sharing process between different smart medical devices. This article first introduces an efficient privacy-preserving model with blockchain to construct a secure data-sharing mechanism between different device nodes. This model utilizes distributed storage form to solve the centralized management problem and provides a fundamental secret reconstruction and retrieval framework. Then, a lightweight$(t,n)$-threshold secret sharing$(t/n$-SS) scheme is designed to strengthen the medical data-sharing security and efficiency. It utilizes the interleaving encode technology to decrease the length of original message into$n$small shares. These small shares are also suitable for data transmission and processing with a more energy-efficient way. It can protect privacy by destroying the data’s semantic meaning. Meanwhile, it only needs less than$t (t\leq n)$shares to recover the original secrets, making the sharing process more efficient. Moreover, the performance evaluations of transaction processing in IoMT show that the proposed model is very stable. The simulation and performance evaluation results show that this$t/n$-SS scheme is energy efficient, storage saving, and strong fault tolerance than similar literature.

Quantum sealed-bid auction (QSA) is expected to provide an alternative to classical sealed-bid auction to defend against attacks from quantum adversaries. However, most previous QSA protocols required bidders to have quantum operation capabilities, resulting in excessive quantum resource requirements. To relieve the burden of protocol quantum resources, a measurement-based quantum sealed-bid auction protocol with the one-way transmission is proposed. Compared to previous protocols, our protocol only requires bidders to perform measurements, and no other operations, such as Pauli or Phase operations, are required. In order to verify the correctness and feasibility of the proposed QSA protocol, the corresponding quantum circuits are designed and simulated by IBM Qiskit. The proposed protocol satisfies security properties, including anonymity, fairness, verifiability, privacy, and non-repudiation. In addition, we further extend the protocol to scenarios that do not require an auctioneer and achieve bid privacy protection for unsuccessful bidders. Therefore, our protocol may have broad applications in auction scenarios.

Electronic medical records (EMRs) are the most critical data in human health management. As in traditional centralized healthcare service systems (HSSs), user privacy security, EMRs data leakage, tampering, and island are some significant problems. However, blockchain is a promising technology to protect the privacy and realize cross-institutional data sharing for solving these problems. In this article, a novel peer-to-peer EMRs data management and trading system called healthchain has been proposed based on consortium blockchain technology. Through this distributed system, the patient can access their EMRs in different institutions freely, and the EMRs can be traded among different users conveniently. Then, to balance EMRs data supply and demand, we establish a Stackelberg pricing model to evaluate EMRs data providers and consumers' interactions. The optimal unit price and data amounts can be found by applying the backward induction method, and the maximizing benefits of the participants can be obtained by achieving the Nash equilibrium in the proposed game. Moreover, security analysis shows the healthchain can provide secure EMRs management and trading, and the simulation results show that the proposed pricing model can help the healthchain achieve social welfare maximization.

An adversary can deploy parasitic sensor nodes into wireless sensor networks to collect radio traffic distributions and trace back messages to their source nodes. Then, he can locate the monitored targets around the source nodes with a high probability. In this paper, a Source-location privacy Protection scheme based on Anonymity Cloud (SPAC) is proposed. We first design a light-weight (t, n)-threshold message sharing scheme and map the original message to a set of message shares which are shorter in length and can be processed and delivered with minimal energy consumption. Based on the shares, the source node constructs an anonymity cloud with an irregular shape around itself to protect its location privacy. Specifically, an anonymity cloud is a set of active nodes with similar radio actions and they are statistically indistinguishable from each other. The size of the cloud is controlled by the preset number of hops that the shares can walk in the cloud. At the border of the cloud, the fake source nodes independently send the shares to the sink node through proper routing algorithms. At last, the original message can be recovered by the sink node once at least t shares are received. The simulation results demonstrate that the SPAC can strongly protect the source-location privacy in an efficient manner. Moreover, the message sharing mechanism of SPAC increases the confidentiality of network data and it also brings high tolerance for the failures of sensor nodes to the data transmission process.