Research on Data Security and Privacy Protection Strategies in Hospital Information Management

Abstract

Abstract Hospital information security, especially the management of hospital information, is of great significance to improve hospital quality, promote resource sharing, and enhance hospital competitiveness. Despite their unique advantages in preventing transmission data leakage when dealing with medical data, federated learning algorithms still have some shortcomings. Based on this, this study proposes to combine the improved TVFedmul algorithm with the federated learning technique to enhance the efficiency of information aggregation and also proposes to utilize the Gaussian difference privacy algorithm to enhance the protection of private data. Four datasets from cancer rehabilitation data are utilized as research samples in experiments. Compared with the FedAvg algorithm, the TVFedmul algorithm is relatively leading in accuracy, e.g., the accuracy enhancement on the same-distribution dataset of renal cancer reaches 3.03%, and the performance enhancement in the C-domain of the non-simultaneous-distribution dataset of breast cancer reaches 14.2%. The TVFedmul algorithm’s model aggregation speed is also faster, which can effectively improve the efficiency of information aggregation. Although the privacy mechanism of the Gaussian differential privacy algorithm affects the accuracy of the model, its accuracy convergence is not much different from that of federated learning without differential privacy, implying that the Gaussian differential privacy algorithm utilizes a small performance loss to provide more valuable privacy protection.