This paper introduces a framework for chaos-based quantum encryption of healthcare images. It enables secure transmission of cipher images from one location to another via the cloud, where healthcare staff can decrypt and assist users securely. The system combines MATLAB and Verilog, utilizing MATLAB for image preprocessing and Verilog for implementing a quantum cryptography algorithm. Images are preprocessed in MATLAB, converted to an encrypted format using Verilog with a quantum cryptography algorithm like BB84, and decrypted on the receiving end. The proposed approach also incorporates a novel quantum image encryption algorithm using gray code and a chaotic map. This integration enables the development of a secure image processing system for healthcare media using quantum cryptography techniques.
This paper introduces a framework for chaos-based quantum encryption of healthcare images. It enables secure transmission of cipher images from one location to another via the cloud, where healthcare staff can decrypt and assist users securely. The system combines MATLAB and Verilog, utilizing MATLAB for image preprocessing and Verilog for implementing a quantum cryptography algorithm. Images are preprocessed in MATLAB, converted to an encrypted format using Verilog with a quantum cryptography algorithm like BB84, and decrypted on the receiving end. The proposed approach also incorporates a novel quantum image encryption algorithm using gray code and a chaotic map. This integration enables the development of a secure image processing system for healthcare media using quantum cryptography techniques.
Security of medical media is important for patient safety and confidentiality. This paper proposes a framework for the chaos-based quantum encryption of healthcare images. In the framework, healthcare staff in one location send cipher images to the cloud. The healthcare staff in another location receives the images from the cloud. By decrypting the content of the images, the healthcare staff can assist users in a secure manner. This paper also proposes a novel approach for the efficient quantum image encryption of healthcare media. The proposed algorithm utilizes gray code and a chaotic map. The quantum image is scrambled by quantum gray code. Then, the scrambled quantum image is encrypted using a quantum XOR operation based on a key generator controlled by the logistic-sine map. The circuits of the proposed encryption/decryption algorithm are devised based on an NEQR quantum image representation. Numerical and simulation analyses show that the proposed quantum image encryption approach is robust, realizable, and has high efficiency compared with its classical counterpart.
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.
Abstract Ensuring the security and privacy of patient data in e‐healthcare systems that rely on cloud computation is of utmost importance. Traditional encryption is no longer resistant to quantum attacks and safeguards sensitive medical images. To tackle this issue, robust security countermeasures are proposed by integrating quantum encryption with a cloud‐based healthcare system. The encryption scheme utilizes the Generalized Novel Enhancement Quantum Representation (GNEQR) and the Novel Enhancement Quantum Representation (NEQR) to provide a framework for representing color and grayscale healthcare images. The proposed quantum algorithm uses quantum logic for image scrambling, which is combined with the encryption key by the Xor quantum gate. The encryption key is generated by 9D chaotic and permutated before encryption. Finally, channel re‐ordering is applied for color images. The simulation results for 15 medical tests with an encryption key space >2600 on a developed e‐healthcare system demonstrate the effectiveness and reliability of the proposed work where the average number of pixels change rates was 99.82, while the unified average change intensity rate was 33.51, entropy was 7.9, the horizontal, vertical, and diagonal correlation coefficients averaged 0.000533333, 0.000706667, and 0.00076, respectively. Finally, the mean squared error (MSE) between the original and encrypted images was 10203.72. These findings improve digital healthcare by revealing the solutions' performance, security, and efficacy.
Quantum computing poses an unparalleled threat to the conventional methodologies of image encryption through its capability for mathematical-based security breaking. This paper discusses the integration of chaos theory with quantum mechanics in the development of robust algorithms for image encryption, considering the inherent vulnerabilities of classical approaches. We start with the basics of chaos theory and its implications in cryptography, emphasizing sensitivity regarding initial conditions and the unpredictability of chaotic systems. Then, we reviewed some of the existing quantum image encryption techniques and their limitations. Our analysis elucidates the potential of chaos-driven quantum algorithms for key generation and image scrambling to enhance security metrics such as key sensitivity, entropy, and unpredictability. We further discuss the performance of such algorithms in resistance against different attack vectors and their application domains, including healthcare, military, and digital rights management. The paper concludes by pointing out the directions of future research, hence calling for further development of sophisticated chaotic maps to integrate them into practical quantum image encryption solutions. The study points out that chaos-based quantum image encryption has strong potential to defend against evolving cyber threats.
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.
Medical image security is crucial for patient confidentiality because it is exposed to many attacks, such as editing photos or moving the affected part to another place, and others. Encryption is an efficient technique to protect the confidentiality of images, but traditional encryption suffers from brute-force attacks. This research proposes an encryption method depending on quantum concepts to resist brute-force attacks using a 3D logistic map to generate keys; the keys were scrambled to increase the randomness, and using the best image scrambling scheme generated by a genetic algorithm as a first stage to increase the strength of the encryption algorithm. The quantum scrambled image is then encrypted by using XOR process depending on a 3D logistic map key generator. The proposed encrypting and decrypting algorithm utilize a NEQR quantum image representation. Numerical and simulation analyses using MATLAB demonstrate that the suggested encryption algorithm is robust, realizable, and highly efficient in comparison to its classical equivalent.