A New Data Model for the Privacy Protection of Medical Images

Benefiting from the intelligent Medical Internet of Things (IoMT), the medical industry has dramatically improved its quality and productivity. The transmission of biomedical data in an open and untrusted network poses a new challenge to the privacy protection of patient information. The low process...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-07, Vol.2022, p.1-12
Main Authors: Ren, Lijing, Zhang, Denghui
Format: Article
Language:English
Subjects:
Algorithms
Archives & records
Artificial intelligence
Biomedical data
Boolean
Communication
Cryptography
Data encryption
Data models
Data security
Information processing
Internet of medical things
Internet of Things
Medical equipment
Medical imaging
Neural networks
Patient education
Patients
Privacy
Privacy, Right of
Recognition
Transfer learning
Venture capital
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Summary:Benefiting from the intelligent Medical Internet of Things (IoMT), the medical industry has dramatically improved its quality and productivity. The transmission of biomedical data in an open and untrusted network poses a new challenge to the privacy protection of patient information. The low processing power of IoMT limited the application of traditional encryption to protect sensitive data. In this paper, we developed a new data protection model for medical images. The model uses visual cryptography (VC) to store biomedical data in a separate database, which can transfer the sensitive data of patients simply and securely. To alleviate the degradation of biomedical recognition performance caused by VC-based noise, we further use transfer learning to train an optimized neural network. The experimental results show that this proposed method provides privacy in the IoMT environment and maintains the high accuracy of biomedical recognition.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/5867215