TY - JOUR
T1 - Application of QR Code Watermarking and Encryption in the Protection of Data Privacy of Intelligent Mouth-Opening Trainer
AU - Liu, Jiannan
AU - Han, Jing
AU - Fu, Kang
AU - Jia, Jun
AU - Zhu, Dandan
AU - Zhai, Guangtao
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/6/15
Y1 - 2023/6/15
N2 - Quick response (QR) codes are widely used in offline to online channels to transfer information from promotional materials to mobile devices. Self-service medical equipment can record the data of each test, so the use of QR codes can realize the data exchange between patients and doctors, medical institutions, and self-service medical equipment, and create a medical information platform for health files. However, since anyone can easily read the information in the QR code, it is not conducive to the protection of patient privacy. Therefore, we propose a QR code encryption and decryption model based on robust digital watermarking. We implement digital watermarking through the generative adversarial networks and increase the robustness of the watermark by adding noise to the model. At the same time, we encrypt and decrypt the QR code information through advanced encryption standards. Experimental results show that the proposed method can well protect the privacy of patients without affecting the data acquisition by patients and doctors.
AB - Quick response (QR) codes are widely used in offline to online channels to transfer information from promotional materials to mobile devices. Self-service medical equipment can record the data of each test, so the use of QR codes can realize the data exchange between patients and doctors, medical institutions, and self-service medical equipment, and create a medical information platform for health files. However, since anyone can easily read the information in the QR code, it is not conducive to the protection of patient privacy. Therefore, we propose a QR code encryption and decryption model based on robust digital watermarking. We implement digital watermarking through the generative adversarial networks and increase the robustness of the watermark by adding noise to the model. At the same time, we encrypt and decrypt the QR code information through advanced encryption standards. Experimental results show that the proposed method can well protect the privacy of patients without affecting the data acquisition by patients and doctors.
KW - Adversarial training
KW - data privacy
KW - information hiding
KW - intelligent medical care
KW - quick response (QR) code
UR - https://www.scopus.com/pages/publications/85148441707
U2 - 10.1109/JIOT.2023.3242319
DO - 10.1109/JIOT.2023.3242319
M3 - 文章
AN - SCOPUS:85148441707
SN - 2327-4662
VL - 10
SP - 10510
EP - 10518
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 12
ER -