ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification
This work addresses privacy-preserving medical data sharing and classification, which is incremental as it builds on existing encryption and ViT methods.
The research tackled the problem of applying deep neural networks to encrypted medical data without compromising performance and security by introducing a secure framework with a learnable encryption method and Vision Transformer integration, achieving robust performance against attacks like leading bit and minimum difference attacks.
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.