Data Security Challenges in Deep Neural Network for Healthcare IoT Systems

Edmond S. L. Ho*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)
6 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationSecurity and Privacy Preserving for IoT and 5G Networks
Subtitle of host publicationTechniques, Challenges, and New Directions
EditorsAhmed A. Abd El-Latif, Bassem Abd-El-Atty, SSalvador E. Venegas-Andraca, Wojciech Mazurczyk, Brij B. Gupta
Place of PublicationCham, Switzerland
Number of pages19
ISBN (Electronic)9783030854287
ISBN (Print)9783030854270, 9783030854300
Publication statusPublished - 2022

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511


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