Structural Image De-Identification for Privacy-Preserving Deep Learning

Structural Image De-Identification for Privacy-Preserving Deep Learning

Due to the risk of data leakage while training deep learning models in a shared environment, we propose a new privacy-preserving deep learning (PPDL) method using a structural image de-identification approach for object classification. The proposed structural image de-identification approach is desi...

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Bibliographic Details
Main Authors: Dong-Hyun Ko, Seok-Hwan Choi, Jin-Myeong Shin, Peng Liu, Yoon-Ho Choi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Data privacy
deep learning
image encryption
structural similarity
vector graphics
Online Access:https://ieeexplore.ieee.org/document/9129656/

Internet

https://ieeexplore.ieee.org/document/9129656/

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