Privacy Risk Assessment of Medical Big Data Based on Information Entropy and FCM Algorithm - Zhang, Xiaoliang; Guo, Tianwei (IEEE access, 2024)
With the rapid development of information technology and the advancement of medical informatization, medical big data plays an increasingly important role in diagnosis, treatment, health management, and other aspects. However, the high sensitivity and privacy of medical data also bring serious security challenges. A privacy risk assessment model combining information entropy and fuzzy C-means clustering algorithm is proposed to address this issue. This model is based on information entropy to construct an access control model and quantify the privacy risks of user access behavior. Cluster analysis is conducted on users using the fuzzy C-means clustering algorithm, and different permissions are assigned based on their access habits. The experimental results show that when the iteration number is 120, the root mean square error value of the improved fuzzy C-means clustering model is 0.08, and the accuracy is 0.98. When the dataset is 100, it can be seen that each model can learn the information in the dataset relatively completely. When the dataset reaches 800, the judgment time of the improved fuzzy C-means clustering model is 0.6 seconds. When the number of users reaches 100, the judgment time of the improved fuzzy C-means clustering model is 1.8 seconds. The research results indicate that the proposed medical big data privacy risk assessment model, which combines information entropy and improved fuzzy C-means clustering algorithm, has excellent performance and can provide new technical means for medical data privacy protection, enhancing the security and reliability of medical information systems.