@inbook{Ho:HealthcareIoT:2022,
author="Ho, Edmond S. L.",
editor="Abd El-Latif, Ahmed A.
and Abd-El-Atty, Bassem
and Venegas-Andraca, Salvador E.
and Mazurczyk, Wojciech
and Gupta, Brij B.",
title="Data Security Challenges in Deep Neural Network for Healthcare IoT Systems",
bookTitle="Security and Privacy Preserving for IoT and 5G Networks: Techniques, Challenges, and New Directions",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="19--37",
abstract="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.",
isbn="978-3-030-85428-7",
doi="10.1007/978-3-030-85428-7_2",
url="https://doi.org/10.1007/978-3-030-85428-7_2"
}