In recent years, the prevalence of medical insurance fraud has significantly impacted the national medical insurance fund. Furthermore, the task of medical insurance fraud detection faces considerable challenges due to the extreme imbalance of positive and negative samples. In practice, health insurance records contain longitudinal and cross-sectional multimodal data. Existing fraud detectors often disregard the multimodal information from heterogeneous neighbors (e.g., time, department, and treatment) and overlook the interaction details among multi-class data entities in claims behavior. To address these challenges, this study proposes the Heterogeneous graph-based Fraud Detection Network(HeFDN) for medical insurance fraud detection, which is based on heterogeneous graphs. The model represents the interaction behaviors of multi-class entities within medical insurance data through heterogeneous graphs. It also introduces a heterogeneous graph target node generator, termed HeGA, to address the issue of node imbalance classification in heterogeneous graphs. Leveraging the heterogeneous graph data structure, the model learns the topological structure and characteristics of actual minority class nodes by generating a set of virtual minority class target nodes. This approach aims to balance the data across different categories, thereby facilitating the distinction of minority class data in high-dimensional space. To validate the model’s effectiveness, this study conducted multiple experiments utilizing a public dataset and a dataset constructed from three real medical insurance sources from hospitals across various levels in Xianning City in China. Experimental results indicate that the proposed model enhances the AUC by an average of 18.2 % and the F1-Score by an average of 26.81 % compared to the most advanced algorithms reviewed.

Hyperledger Fabric, which satisfies decentralization and security, is one of the most popular blockchain distributed ledger technologies, but in high-throughput, high-concurrency distributed scenarios, concurrency conflicts can cause transactions to fail, resulting in significant network bandwidth and resource waste. In this paper, we deeply analyze the operation flow of Fabric’s underlying transactions, and propose a solution to concurrent transaction conflicts in Fabric based on distributed locks and message queues. Redis is used to implement the locking mechanism of concurrent keys, and the ChaincodeEvent event listener is used to detect the transactions and realize the unlocking operation; on this basis, the message asynchronous retry mechanism, i.e., use the message queue RabbitMQ to re-consume the conflicting transactions to solve the concurrent transaction conflicts encountered against Fabric at present. Experiments show that the proposed approach can significantly improve the concurrency capability of the system, including transaction success rate and effective transaction throughput, under different levels of transaction conflicts; it does not affect the performance of the system in the absence of transaction conflicts.

The number of theft cases is much higher than that of other criminal cases, which frequently occurs in daily life and is seriously destructive to social order. Studying the law of theft cases has a positive impact on social governance and optimizing police deployment. Therefore, based on the data of theft cases in H city, this study proposes an optimized decomposition and fusion method based on XGBoost , and establishes two multi-classification prediction models, such as OVR-XGBoost and OVO-XGBoost. As the theft data is a datasets with unbalanced class distribution, this paper uses SMOTENN algorithm to process it into a datasets with balanced distribution, which effectively improves the effect of the model. Experiments show that the prediction accuracy of OVR-XGBoost and OVO-XGBoost models is higher than that of baseline XGBoost models. For categories with few samples, the classification effect of OVO-XGBoost is better than that of baseline XGBoost and OVO-XGBoost models. Compared with baseline XGBoost model, the average overall classification accuracy of OVO-XGBoost model is improved by more than 7%, and the MacroR accuracy is also improved by more than 15%. The model proposed in this study has a good effect on the classification and prediction of theft types, and is of great significance for the prevention of theft cases.

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.

Summary Singular value decomposition (SVD) shows strong vitality in the area of information analysis and has significant application value in most of the scientific big data fields. However, with the rapid development of Internet, the information online reveals fast growing trend. For a large‐scale matrix, applying SVD computation directly is both time consuming and memory demanding. There are many works available to speed up the computation of SVD based on the message passing interface model. However, to deal with large‐scale data processing, a MapReduce model has many advantages over a message passing interface model, such as fault tolerance, load balancing and simplicity. For a MapReduce environment, existing approaches only focus on low rank SVD approximation and tall‐and‐skinny matrix SVD computation, and there are no implementations of full rank SVD computation. In this paper, we propose a MapReduce‐based implementation for solving divide‐and‐conquer SVD algorithm. To achieve high performance, we design a two‐stage task scheduling strategy based on the mathematical characteristics of divide‐and‐conquer SVD algorithm. To further strengthen the performance, we propose a row‐index‐based divide algorithm, a pipelined task scheduling method, and revised block matrix multiplication in MapReduce framework. Experimental result shows the efficiency of our algorithm. Our implementation can accommodate full rank SVD computation of large‐scale matrix very efficiently. Copyright © 2014 John Wiley & Sons, Ltd.

By combining cloud computing and Peer-to-Peer computing, a P2P storage cloud can be formed to offer highly available storage services, lowering the economic cost by exploiting the storage space of participating users. However, since cloud severs and users are usually outside the trusted domain of data owners, P2P storage cloud brings forth new challenges for data security and access control when data owners store sensitive data for sharing in the trusted domain. Moreover, there are no mechanisms for access control in P2P storage cloud. To address this issue, we design a ciphertext-policy attribute-based encryption (ABE) scheme and a proxy re-encryption scheme. Based on them, we further propose a secure, efficient and fine-grained data Access Control mechanism for P2P storage Cloud named ACPC. We enforce access policies based on user attributes, and integrate P2P reputation system in ACPC. ACPC enables data owners to delegate most of the laborious user revocation tasks to cloud servers and reputable system peers. Our security analysis demonstrates that ACPC is provably secure. The performance evaluation shows that ACPC is highly efficient under practical settings, and it significantly reduces the computation overheads brought to data owners and cloud servers during user revocation, compared with other state-of-the-art revocable ABE schemes.