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Journal of Information Security and Applications

A secure medical image transmission scheme aided by quantum representation

T. Janani, M. Brindha

Published Apr 26, 2021• 50 citations
Publisher
DOI
10.1016/j.jisa.2021.102832

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In the age of digital technology, medical images play an important role in the healthcare industry, which aids surgeons in making precise decisions and reducing the diagnosis time. However, the storage of large amounts of these images in third-party cloud services raises privacy and security concerns. There are a lot of classical security mechanisms to protect them. Although, the advent of quantum computing entails the development of quantum-based encryption models for healthcare. There is a significant demand for resource-efficient, quantum-secure image encryption mechanisms that leverage existing classical infrastructure. Hence, in this paper, a novel quantum chaos-based encryption scheme for medical images has been introduced. The model utilizes bit-plane scrambling, a 3D quantum logistic map, quantum operations in the diffusion phase, a hybrid chaotic map, and DNA encoding in the confusion phase to transform the plain medical image into a cipher medical image. The proposed scheme has been evaluated using multiple statistical analyses and validated against more attacks such as differential attacks, known and chosen plaintext attacks with three different medical datasets, and theoretically, as well. Hence, the introduced encryption model has proved to be more attack-resistant and robust than other existing image encryption schemes, ensuring the secure storage of medical images in cloud environments.
Secure transmission of encrypted medical images demands trustworthy identification of the used encryption method, especially in settings where the availability of metadata is uncertain or unreliable. In this paper, a quantum-enhanced AI framework for the classification of encryption methods used in medical image Regions of Interest (ROIs) is introduced. After the extraction of the ROI and chaotic map-based encryption, a hybrid quantum-classical learning strategy is used to classify the encryption method without depending on any metadata. The proposed quantum AI framework combines variational quantum circuits as efficient latent feature encoders in CNN architectures. The high-dimensional CNN features are then compressed into low-dimensional latent spaces before the quantum embedding process, enabling the classification task to be conducted on the highly compressed feature representation. Quantum-enhanced ResNet18, VGG11, and EfficientNet-B0 models were trained and tested on encrypted ROIs extracted from dermoscopic medical images. The experimental results show that the proposed framework can successfully distinguish between different encryption methods, confirming the feasibility of quantum-assisted cipher classification for secure transmission of medical images. This work shows the potential of quantum-enhanced feature representation in privacy-preserving healthcare applications and automatic decryption routing in medical systems.