AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach

TL;DR

This paper presents a novel AI-driven framework that combines learnable encryption with Vision Transformer models to enable secure, privacy-preserving data sharing, especially for sensitive medical datasets, while maintaining high performance and robustness against attacks.

Contribution

It introduces a unique encryption method integrated with ViT, ensuring data privacy and security without sacrificing computational efficiency or accuracy.

Findings

Achieved 94% success rate on real-world medical datasets.

Demonstrated robustness against diverse adversarial attacks.

Validated effectiveness in secure medical data sharing scenarios.

Abstract

In the era of data-driven decision-making, ensuring the privacy and security of shared data is paramount across various domains. Applying existing deep neural networks (DNNs) to encrypted data is critical and often compromises performance, security, and computational overhead. 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 adversarial attacks without compromising computational efficiency and data integrity. The framework was tested on sensitive medical datasets to validate its efficacy, proving its ability to handle highly confidential information securely. The suggested framework was validated with a 94\% success rate after extensive testing on real-world datasets, such as MRI brain tumors and histological scans of lung and colon cancers. Additionally, the framework was tested under diverse adversarial attempts against secure data sharing with optimum performance and demonstrated its effectiveness in various threat scenarios. These comprehensive analyses underscore its robustness, making it a trustworthy solution for secure data sharing in critical applications.