Report
ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification
| العنوان: | ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification |
|---|---|
| المؤلفون: | Amin, Al, Hasan, Kamrul, Ullah, Sharif, Hossain, M. Shamim |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 2 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2411.05901 |
| رقم الانضمام: | edsarx.2411.05901 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |