ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

TL;DR

This research introduces a secure framework consisting of a learnable encryption method based on block-pixel operation to encrypt the data and subsequently integrate it with the Vision Transformer (ViT) to ensure data privacy and security.

Abstract

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.

Figures (2)

• Figure 1: Overview of ViT integrated privacy-preserving secure medical data sharing and classification framework.
• Figure 2: Visual Representation of Transformation Pipeline: Original, Encrypted, and Post-Attack Images.