Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical image...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 27; H. 2; S. 804 - 813
Hauptverfasser: Han, Baoru, Jhaveri, Rutvij H., Wang, Han, Qiao, Dawei, Du, Jinglong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Collaborative work
Computer Security
Data privacy
Delivery of Health Care
Dermatology
Discrete cosine transform
Feature extraction
Federated learning
Health care
Humans
Image acquisition
IoMT
Machine learning
Medical imaging
Medical services
Privacy
Robustness
Security
Servers
sparse autoencoder network
Watermarking
Zero-watermarking
ISSN:2168-2194, 2168-2208, 2168-2208
Online-Zugang:Volltext
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Zusammenfassung:The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2021.3123936