Background: With the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required. Objective: Our goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients’ privacy in the EMRs through a medical information exchange process that includes data encryption and access control. Methods: We propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient’s EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework. Results: We performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system’s performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption–based data encryption method can effectively protect users’ EMRs from forgery and privacy leak threats and provide data integrity. Conclusions: Blockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.

Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.

With the development of blockchain technology, participants need to have huge storage volumes to deal with the growing blockchain ledger size over time. This requirement leads to the conditional participation and verification of participants, thus weakening the decentralization of a blockchain system. Several compression schemes have been proposed to mitigate this storage problem by compressing a blockchain ledger based on redundancy, modular functions, and hash functions. However, these schemes have the limitation of accumulating the compression results to validate the retained blocks. The accumulation gradually reduces the storage volume for the blockchain ledger within the storage volume of nodes with limited resources, thus reducing the verification capability of the nodes. In this paper, a selective compression scheme using a checkpoint-chain is proposed to prevent the accumulation of compression results. The checkpoint-chain is a second blockchain that stores the checkpoints compressing existing blocks through a block Merkle tree. An update process is also proposed to prevent the accumulation of checkpoints by combining them. As numerous blocks can be verified with only a few updated checkpoints, blockchain nodes with limited resources can reduce the storage volume for the blockchain ledger and achieve high verification capabilities. Finally, compared with the existing compression schemes, the proposed scheme can achieve an average reduction in the storage overhead and an average increase in the verification capability of 76.02% and 13.90%, respectively. Moreover, the corresponding performance improvements are 86.14% and 15.44% when the update process is performed, respectively.

Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users’ assets, threaten the network’s reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users’ assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

Despite the widespread success of convolutional deep learning frameworks in computer vision, significant limitations persist in medical image analysis. These include low image quality caused by noise and artifacts, limited data availability compromising robustness on unseen data, class imbalance leading to biased predictions, and insufficient feature representation, as conventional CNNs often fail to capture subtle patterns and complex dependencies. To address these challenges, we propose DAME (Diffusion-Augmented Meta-Learning Ensemble), a unified architecture that integrates hybrid modeling with generative learning using the Denoising Diffusion Probabilistic Model (DDPM). The DDPM component improves resolution, augments scarce data, and mitigates class imbalance. A hybrid backbone combining CNN, Vision Transformer (ViT), and CBAM captures both local dependencies and long-range spatial relationships, while CBAM further enhances feature representation by adaptively emphasizing informative regions. Predictions from multiple hybrids are aggregated, and a logistic regression meta classifier learns from these outputs to produce robust decisions. The framework is evaluated on the HAM10000 dataset, a benchmark for multi-class skin cancer classification. Explainable AI is incorporated through Grad CAM, providing visual insights into the decision-making process. This synergy mitigates CNN limitations and demonstrates superior generalizability, achieving 98.6% accuracy, 0.986 precision, 0.986 recall, and a 0.986 F1-score, significantly outperforming existing approaches. Overall, the proposed framework enables accurate, interpretable, and reliable medical image diagnosis through the joint optimization of contextual modeling, feature discrimination, and data generation.