As a network structure composed of several wearable or implantable devices as well as their transmission nodes and processing nodes, Wireless Body Area Network(WBAN) is one of the important application directions of the medical Internet of Things(IoT). Devices in the network collect physiological data from users and send it to the remote medical servers by the wireless technology. Then, the health-care provider accesses the server through the network, so as to provide services to the wearers. However, due to the openness and mobility of the wireless network, if the information in the WBAN is stolen, forged or attacked in the channel, the wearers' privacy will be leaked, even the personal security of the users will be endangered. The research on privacy protection mechanisms in WBAN was reviewed, and on the basis of analyzing the data transmission characteristics of the network, the privacy protection mechanisms based on authentication, encryption and biological signals were summarized, and the advantages and disadvantages of these mechanisms were compared, so as to provide a reference to the enhancement of prevention awareness and the improvement of prevention technology in WBAN applications.

The traditional methods of manual heart disease monitoring are highly dependent on senior doctors with prior knowledge, and their speeds and accuracies of monitoring disease need to be improved. In order to solve these problems, a ElectroCardioGraph (ECG) monitoring algorithm based on stack classifier was proposed for the determination of cardiac anomalies. Firstly, the advantages of various machine learning algorithms were combined, and these algorithms were integrated by the way of stack classifier to make up for the limitation of learning by single machine learning algorithm. Then, Synthetic Minority Over-sampling TEchnique (SMOTE) was used to perform data augmentation to the original dataset and balance the number of samples of various diseases, so as to improve the data balance. The proposed algorithm was compared with other machine learning algorithms on MIT-BIH dataset. Experimental results show that the proposed algorithm can improve the accuracy and speed of abnormal ECG monitoring.