Paper Info
Title
PE-HEALTH: Enabling Fully Encrypted CNN for Health Monitor with Optimized Communication
Abstract
Cloud-based Convolutional neural network (CNN) is a powerful tool for the healthcare center to provide health condition monitor service. Although the new service has future prospects in the medical, patient's privacy concerns arise because of the sensitivity of medical data. Prior works to address the concern have the following unresolved problems: 1) focus on data privacy but neglect to protect the privacy of the machine learning model itself; 2) introduce considerable communication costs for the CNN inference, which lowers the service quality of the cloud server. To push forward this area, we propose PE-HEALTH, a privacy-preserving health monitor framework that supports fully-encrypted CNN (both input data and model). In PE-HEALTH, the medical Internet of Things (IoT) sensor serves as the health condition data collector. For protecting patient privacy, the IoT sensor additively shares the collected data and uploads the shared data to the cloud server, which is efficient and suited to the energy-limited IoT sensor. To keep model privacy, PE-HEALTH allows the healthcare center to previously deploy, and then, use an encrypted CNN on the cloud server. During the CNN inference process, PE-HEALTH does not need the cloud servers to exchange any extra messages for operating the convolutional operation, which can greatly reduce the communication cost.
Year
DOI
Venue
2020
10.1109/IWQoS49365.2020.9212822
2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)
Keywords
DocType
ISBN
Health Condition Monitor,CNN,Privacy-Preserving,Medical IoT Sensors
Conference
978-1-7281-6887-6
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Yang Liu1136.37
Yilong Yang200.34
Zhuo Ma3235.12
Ximeng Liu413531.84
Zhuzhu Wang5103.17
Siqi Ma600.34