SecureNet: A deep learning inspired security framework for healthcare data

Abstract

As medical devices become increasingly interconnected through the Internet of Medical Things (IoMT), safeguarding image data against unauthorized access and tampering has become a pressing challenge. Many existing solutions fall short in balancing computational efficiency with robust encryption, particularly when high-fidelity recovery of diagnostic images is required. This work presents SecureNet, a hybrid encryption framework tailored for medical imaging applications. This work proposes SecureNet, a compact and resilient encryption framework designed for secure medical image transmission. SecureNet leverages a convolutional autoencoder to extract compact latent features, applies spatial rearrangement using a Hilbert curve to disrupt pixel locality, and diffusion with a chaos-driven random projection. The result is a highly randomized and distorted representation, complicating any attempt at unauthorized analysis. The encrypted data can be accurately recovered through symmetric decryption operations. Experimental results, supported by security and comparative analyses, validate the effectiveness and generalizability of the proposed framework in resisting various attacks, demonstrating its encryption efficacy and performance on par with state-of-the-art approaches. The framework presents a scalable solution for securing healthcare data in dynamic and resource-constrained IoMT environments.