With the advancement of IoT technology, more and more healthcare applications were developed in recent years. In addition to the traditional sensor-based systems, image-based healthcare IoT systems become more popular since no specialized sensors are required. Combining with Deep Neural Network (DNN) based automated diagnosis and decision-making systems, it is possible to provide users with 24/7 health monitoring in real life. However, the high computational cost for training DNNs can be a hurdle for developing such kind of powerful systems. While cloud computing can be a feasible solution, uploading training data for the DNN models to the cloud may lead to data security issues. In this chapter, we will review some image-based healthcare IoT systems and discuss some potential risks on data security when training the DNN models on the cloud.
|Title of host publication||Security and Privacy Preserving for IoT and 5G Networks|
|Subtitle of host publication||Techniques, Challenges, and New Directions|
|Editors||A.A. Abd El-Latif, B. Abd-El-Atty, S.E. Venegas-Andraca, W. Mazurczyk, B.B. Gupta|
|Place of Publication||Cham, Switzerland|
|Publication status||Accepted/In press - 26 Apr 2021|