ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910692171317248 |
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| author | Amin, Al Hasan, Kamrul Ullah, Sharif Hossain, M. Shamim |
| author_facet | Amin, Al Hasan, Kamrul Ullah, Sharif Hossain, M. Shamim |
| contents | Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on the block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification Amin, Al Hasan, Kamrul Ullah, Sharif Hossain, M. Shamim Cryptography and Security Computer Vision and Pattern Recognition Machine Learning Privacy-preserving and secure data sharing are critical for medical image analysis while maintaining accuracy and minimizing computational overhead are also crucial. Applying existing deep neural networks (DNNs) to encrypted medical data is not always easy and often compromises performance and security. To address these limitations, this research introduces a secure framework consisting of a learnable encryption method based on the block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT). The proposed framework ensures data privacy and security by creating unique scrambling patterns per key, providing robust performance against leading bit attacks and minimum difference attacks. |
| title | ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification |
| topic | Cryptography and Security Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.05901 |