Privacy Protection Methods Research for Healthcare Big Data Based on FCM Clustering Algorithm

With the rapid growth of healthcare big data, protecting data privacy has become an important challenge. This study aims to apply the fuzzy C-means clustering algorithm for clustering analysis of health and medical big data and use methods such as risk quantification and access control to control user behavior. The experimental results showed that when the data volume reached 200000, the fuzzy C-means clustering model exhibited optimal performance, taking only 15.9 milliseconds. Meanwhile, when processing the same amount of data, the model had the shortest cumulative time, only 7.6 minutes. Compared with the statistical analysis model, this model not only performed well but also had lower CPU usage. Doctors, researchers, and insurance company personnel have drawn different conclusions regarding the risk limits of different restrictions. In addition, the model can implement differentiated access behavior control for users who trust directly and indirectly based on the difference in trust level, demonstrating its strong ability in data encryption. This method not only protects data privacy, but also maintains good data quality, providing a new solution for the privacy protection of healthcare big data, and is of great significance for research and practice in related fields. © (2024), (International Journal of Network Security). All rights reserved.