# ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification

arXiv: https://arxiv.org/abs/2411.05901 · HTML: https://mlsanity.com/paper/2411.05901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Block-wise Scrambled Image Recognition Using Adaptation Network](https://mlsanity.com/paper/2001.07761/) (2020)
- [RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.05500/) (2023)

## Built on by (most cited first, 1 of 1)

- [Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning](https://mlsanity.com/paper/2412.07454/) (2024)

## Abstract (arXiv)

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.