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International Journal of Theoretical Physics

Quantum Selective Encryption for Medical Images

Shahrokh Heidari, Mosayeb Naseri, Koji Nagata

Published Oct 7, 2019• 32 citations
Publisher
DOI
10.1007/s10773-019-04258-6

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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.
Ensuring the secure storage and sharing of medical records in the cloud is increasingly crucial due to vulnerabilities in classical encryption methods, such as key manipulation and cloud collusion. This paper introduces a novel quantum-based approach using Quantum Fourier Transform (QFT) and Unitary operators. We designed three quantum circuits for different image sizes: one for the Unitary operator, one for encryption, and one for decryption. DICOM images are first transformed into quantum images with the NEQR algorithm, then encrypted using a Unitary operator, which serves as the encryption key. This key is securely transmitted via quantum teleportation, enhancing protection against classical and quantum attacks. Extensive simulations show the method's effectiveness and robustness in safeguarding medical data during cloud storage and transmission. This makes it a promising solution for secure medical image sharing in quantum communication networks.