With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

In recent years, sentiment analysis in conversation has garnered increasing attention due to its widespread applications in areas such as social media analytics, sentiment mining, and electronic healthcare. Existing research primarily focuses on sequence learning and graph-based approaches, yet they overlook the high-order interactions between different modalities and the long-term dependencies within each modality. To address these problems, this paper proposes a novel hypergraph-based method for multimodal emotion recognition in conversation (MER-HGraph). MER-HGraph extracts features from three modalities: acoustic, text, and visual. It treats each modality utterance in a conversation as a node and constructs intra-modal hypergraphs (Intra-HGraph) and inter-modal hypergraphs (Inter-HGraph) using hyperedges. The hypergraphs are then updated using hypergraph convolutional networks. Additionally, to mitigate noise in acoustic data and mitigate the impact of fixed time scales, we introduce a dynamic time window module to capture local-global information from acoustic signals. Extensive experiments on the IEMOCAP and MELD datasets demonstrate that MER-HGraph outperforms existing models in multimodal emotion recognition tasks, leveraging high-order information from multimodal data to enhance recognition capabilities.

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.

Privacy protection in data publishing is an extremely important issue that has been the focus of extensive research in recent years. However, the existing methods have a host of limitations, especially for high-dimensional data publishing. Aiming at the problem of poor availability of publishing results caused by “the curse of dimensionality” in high-dimensional data publishing, we present PPDP-PCAO (Privacy Preserving Data Publishing with Principal Component Analysis Optimization) method, which can better address the problem of the lower utility of release results because of the high noise introduced by the curse of dimensionality. PPDP-PCAO improves the Principal Component Analysis (PCA) algorithm by employing the attribute importance, and reduces the dimension of the data with the improved PCA, which reduces the time and space cost. PPDP-PCAO introduces the evaluation mechanism based on mutual-information into data release, which evaluates the data generated by setting the different quantities of principal components to determine the optimal quantities. PPDP-PCAO considers the existence of multi-sensitive attributes in high-dimensional data, while the traditional methods of allocating privacy budgets cannot satisfy the personalized privacy protection. PPDP-PCAO introduces the sensitivity preference, combines the optimal matching theory, and designs the sensitive attribute hierarchical protection strategy. Extensive experimental results on different real datasets demonstrate that PPDP-PCAO not only guarantees the privacy of published dataset, but also significantly improves the accuracy and data utility than other high-dimensional data publishing methods.

In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.

Federated learning is a distributed machine-learning technique that allows multiple clients to collaboratively train a model without sharing their local raw data. In existing federated learning solutions, one-shot federated learning is a promising yet challenging direction. It involves model training with just one round of communication between clients and the server, reducing communication overheads and security risks. However, most existing methods still face several challenges. First, generating additional data for training prolongs the model training time. Second, the traditional single model cannot handle data heterogeneity in real world scenarios. To address these issues, this article proposes a personalized federated learning method called FedOM under the one-communication premise. Considering the high data heterogeneity, FedOM abandons the traditional single global model architecture and generates multiple group models to overcome the generalization limitations of a single model. Additionally, based on FedOM, this study proposes FedOMF, a personalized method with a fine-tuning module. Experiments on public datasets show that in highly heterogeneous data scenarios, the proposed methods outperform baseline methods, demonstrating superior performance and great practical potential.