Structural Image De-Identification for Privacy-Preserving Deep Learning

Dong Hyun Ko, Seok Hwan Choi, Jin Myeong Shin, Peng Liu, Yoon Ho Choi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number9129656
Pages (from-to)119848-119862
Number of pages15
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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