Barriers to accessing paediatric critical care and surgery: A qualitative study.

DBSCAN is a classic density-based spatial clustering algorithm, which can partition areas of sufficiently high density into clusters and discover arbitrarily shaped clusters in spatial databases with noise []. DBSCAN is a powerful tool that is applied in many applications, such as industrial applications [], analysis of ship traffic behavior [], market analysis [], medical data processing [], urban construction planning [], network security, remote sensing, Many scholars have proposed different improved algorithms, such as parameter optimization, identification and processing of clusters with different densities, big data processing, high-dimensional data processing, and optimization for specific application scenarios. With the rapid development of edge intelligence and mobile Internet, clustering algorithms have encountered new problems: how to perform clustering distributed data under the condition of privacy protection? DBSCAN also faces this problem, which is the motivation of our research. We try to make DBSCAN clustering for distributed data under privacy requirements. An emerging clustering framework provides the answer, namely secure federated clustering as shown in : Each client uploads the desensitized data to the server, and the server sends the processed results to each client. Each client forms the final clusters based on the received results [,].