Structural Image De-Identification for Privacy-Preserving Deep Learning

• Published in: IEEE access Vol. 8; pp. 119848 - 119862
• Main Authors: Ko, Dong-Hyun, Choi, Seok-Hwan, Shin, Jin-Myeong, Liu, Peng, Choi, Yoon-Ho
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Computational modeling; Data models; Data privacy; Deep learning; Encryption; Environment models; Identification; Identification methods; Image classification; image encryption; Machine learning; Object recognition; Privacy; structural similarity; vector graphics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3005911

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 designed based on the fact that the degree of structural distortion of an image object has the greatest impact on human's perceptual system. Thus, by modifying only the structural parts of the original one using order preserving encryption(OPE), the proposed structural image de-identification approach decreases only the recognition rate by human. From the experimental results using different standard datasets, we show that the object classification accuracy of the proposed structural image de-identification method is almost the same as the deep learning performance for non-encrypted images, without revealing the original image contents including sensitive information. Also, by handling the trade-off between object classification accuracy and privacy protection for the de-identified image, we experimentally find the optimal size of input image for the proposed structural image de-identification approach.