The rapid growth of UAV-enabled low-altitude networks (LANs) increases the demand for secure data sharing to support mission-critical applications, yet existing solutions struggle with LANs dynamics, quantum vulnerabilities of traditional cryptography, and unbalanced accountability-privacy. To address these gaps, a blockchain-based privacy-preserving (BCPP) model has been introduced for UAV data sharing in LANs, with three key components: 1) a consortium blockchain optimized for UAV mobility, which replaces centralized trust with distributed consensus to ensure data integrity without single-point dependencies; 2) a novel lattice-based certificateless undeniable signature (CL-US) scheme built on the Ring-Learning With Errors (Ring- LWE) problem—this scheme binds UAV senders/receivers to data transmission for accountability while using pseudonymous identifiers to protect identity privacy. Formal security analysis under the random oracle model (ROM) proves that the proposed CL-US scheme achieves soundness and unforgeability against adaptive chosen-message attacks under the hardness assumption of the Ring-LWE problem. The performance evaluations of the key size and time consumption show that this CL-US scheme is efficient and storage-saving to similar schemes. This work provides a quantum-resilient solution balancing non-repudiation, privacy, and efficiency for dynamic LANs, laying a foundation for secure post-quantum LANs deployment in mission-critical scenarios.

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.

Cold chain logistics systems (CCLSs) require the secure, efficient, and fresh management of cold products. Blockchain technology facilitates cross - institutional data - sharing for CCLSs, yet heterogeneous blockchains across different institutions give rise to ’data islands’ and privacy issues. Facing these problems,we propose a cross-chain privacy-preserving (CCPP) framework based on a notary mechanism. This CCPP model aggregates heterogeneous institutional chains through a notary network, enabling inter-blockchain operability while eliminating the data islands. Meanwhile, to ensure transaction security and address the quantum-vulnerability of traditional signature schemes, we design an identity-based ring signature (ID-RS) scheme. The ID - RS combines the identity mechanism (for traceability) with the ring mechanism (to guarantee the signer’s unconditional anonymity), and combats quantum threats by relying on the lattice assumption (a foundation for post-quantum security). Under the random oracle model, we prove the correctness, unforgeability, and anonymity of the ID-RS scheme. Additionally, experimental results under 80-bit security settings show that our ID-RS scheme outperforms other signature schemes in signature size and verification latency. These findings validate that the CCPP framework and the ID-RS scheme together offer an efficient and practical foundation for privacy-preserving, quantum-resistant data sharing among heterogeneous blockchain-based CCLSs.

Blockchain-enabled Internet of Medical Things (BIoMT) has received extensive attention and in-depth research to solve the centralized, data island problems with the rapid developments of blockchain-related technologies. However, many different chains with different data structures, consensus protocols, and cryptographic algorithms are constructed, which brings a new “data island” problem. Meanwhile, the cryptographic algorithms used in most current BIoMT systems are weak against quantum attacks. In this article, a cross-chain privacy-preserving (CCPP) model and a designated verifier proxy signature (DVPS) scheme have been proposed. This CCPP model is equipped with the relay chain technology and DVPS to achieve secure cross-chain medical data-sharing among different BIoMT systems. The DVPS scheme is constructed with lattice theory, which can achieve signer proxy, designated user verification, and anti-quantum attack. Then, the security proof shows that the proposed DVPS can capture the security properties of correctness, unforgeability, the signer’s anonymity, and nontransferability. The performance evaluations show that the cross-chain transactions are efficient and stable with the transaction number increasing, and the proposed DVPS is efficient about the key size, time consumption, and energy consumption. This work can also improve the privacy security of system users and medical data in BIoMT systems and promote the value play of medical data.

Blockchain technology brings a method for cross-institution health data sharing through the systems of the Internet of Medical Things (IoMT). As different medical institutions compete to establish their own blockchain ledgers, it leads to new problems of “data island”. In this paper, a relay chain-based multi-chain fusion (MCF) model has been designed for blockchain-enabled IoMT (BIoMT), which can achieve cross-institution health data sharing by composing different blockchains together. In this MCF model, the existing patient private health chain, medical institution chain, and government supervision chain compose a cross-chain health data-sharing platform, which extends the storage capacity of health data, and the capacity of data sharing among different departments, institutions, and fields. Meanwhile, a cross-chain transaction model has been established which helps to achieve secure cross-chain transactions among different medical institutions. Then, to guarantee user privacy in the cross-chain transaction process, a designated verifier signature (DVS) scheme is proposed. Only the designated verifier can verify this DVS and other users cannot identify the real signer. This DVS also can achieve the anonymity of the signer as the third party cannot distinguish the signature generated by the signer or the verifier. Moreover, the proposed DVS scheme can be proved to capture the unforgeability, non-transferability, and signer anonymity with the random oracle model. The theoretical analyses and efficiency comparisons are given which show the efficiency of the proposed DVS scheme compared with similar schemes. The performance simulation of the cross-chain transaction shows that the MCF model is secure and practical for cross-chain health data sharing among different BIoMT systems.

Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial-temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.

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.

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.

There has been little investigation for the automatic extraction of power lines from aerial images due to the low resolution of aerial images in the past decades. With increasing aerial photogrammetric technology and sensor technology, it is possible for photogrammetrists to monitor the status of power lines. This letter analyzes the property of imaged power lines and presents an algorithm to automatically extract the power line from aerial images acquired by an aerial digital camera onboard a helicopter. This algorithm first uses a Radon transform to extract line segments of the power line, then uses the grouping method to link each segment, and finally applies the Kalman filter technology to connect the segments into an entire line. We compared our algorithm with the line mask detector method and the ratio line detector, and evaluated their performances. The experimental results demonstrated that our algorithm can successfully extract the power lines from aerial images regardless of background complexity. This presented method has successfully been applied in China National 863 project for power line surveillance, 3-D reconstruction, and modeling.