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.
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.
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.
The rapid evolution of Internet of Things (IoT)-enabled healthcare systems has significantly increased the generation, transmission, and storage of medical images such as MRI, CT scans, and X-rays in cloud environments. While this advancement enhances remote diagnostics and telemedicine, it also introduces critical security challenges related to confidentiality, integrity, and privacy of sensitive patient data. Traditional cryptographic methods, including AES and RSA, are often inadequate in addressing the complex requirements of medical image security due to high computational overhead and vulnerability to emerging cyber threats. Consequently, recent research has shifted toward hybrid approaches integrating chaos theory, DNA encoding, deep learning, and quantum computing. This survey paper provides a comprehensive analysis of modern techniques and architectures for secure medical image cryptanalysis, with a particular focus on quantum neural networks (QNNs) in IoT-enabled cloud environments. The study reviews state-of-the-art encryption schemes such as hyperchaotic systems, quantum key distribution, and hybrid quantum-classical cryptography, highlighting their effectiveness in securing medical image transmission and storage. Additionally, the role of deep learning-based cryptographic models and quantum-inspired algorithms in improving robustness against attacks is examined. The paper further presents a comparative analysis of recent studies (2020–2023), identifying key performance metrics such as entropy, PSNR, computational efficiency, and resistance to statistical and differential attacks. Challenges including resource constraints of IoT devices, scalability issues, and limitations of current quantum hardware are also discussed. Finally, the paper outlines future research directions emphasizing the integration of quantum neural networks and lightweight cryptographic frameworks to ensure secure, efficient, and scalable healthcare 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.
Quantum computing introduces major threats to conventional image encryption methods, especially in medical contexts. This paper addresses these threats by developing a quantum-resistant encryption scheme for medical images. We present a novel framework combining: (1) a novel Mixed Logistic-Ikeda-Henon (MLIH) chaotic map for pseudorandom key generation, (2) quantum image representation using the Novel Enhanced Quantum Representation (NEQR) model, and (3) a two-stage encryption process employing Controlled-Not (CNOT) gate chaining for diffusion and One-Time Pad (OTP) with MLIH-generated keys for confusion. The RGB channels are processed separately through quantum state conversion, CNOT-based diffusion, and keyed confusion before final recombination. To validate practical feasibility, the proposed encryption scheme was implemented on IBM’s 127-qubit ibm_sherbrooke quantum processor, demonstrating real-world feasibility. Experimental validation shows near-ideal entropy (7.9977), superior NPCR (99.97%) and UACI (33.89%) values, and an expansive key space (2 1952 ). The novel MLIH demonstrates a 12.7% improvement in logic gate efficiency compared to conventional chaotic and the image encryption has quantum advantage through parallel CNOT operations. The hardware execution yielded a throughput of 4,500 Circuit Layer Operations Per Second (CLOPS), indicating efficient real-time performance on NISQ devices, Moreover, the echoed cross-resonance (ECR) gate error remained within a median of 1.1 × 10⁻ 2 , supporting reliable circuit execution. The proposed scheme outperforms contemporary quantum and classical encryption approaches in terms of entropy, NPCR, UACI, and key sensitivity, all while maintaining a computational complexity of O(n), ensuring scalability. This study effectively bridges the gap between theoretical quantum security models and real-world implementation on existing NISQ devices, demonstrating resilience against classical statistical and differential attacks, as well as quantum-specific threats such as Grover’s brute-force search and quantum chosen-plaintext attacks. The successful deployment of IBM quantum hardware positions this scheme as a viable solution for secure medical image transmission in quantum-era healthcare systems.
With the growing demand for highly secure medical data transmission, this research introduces a fused cryptography framework that integrates mathematical quantum computing operations with advanced classical encryption techniques. The proposed method incorporates quantum principles, including quantum walks, quantum-based cyclic shift operators, quantum XOR operations, and quantum key image generation with classical methods such as bit-plane extraction and hyperchaotic system-based scrambling. A hyperchaotic map generates random key sequences to produce both a spatial domain random image and a quantum key image. The medical image is first decomposed into eight bit-planes, and only the high-information bit-planes (HIBPs) undergo encryption to optimize computational efficiency. HIBPs are scrambled using multilayer, block-wise, and diagonal permutations based on the chaotic sequences. Quantum encryption is then applied, starting with novel enhanced quantum representation encoding, followed by Baker map-based scrambling and quantum XOR diffusion to secure the final ciphertext. Extensive experiments, including entropy evaluation, noise attack resilience, clipping attack robustness, and correlation analysis, confirm the superior security and performance of the proposed method. Notably, the algorithm achieves near-ideal results, such as an entropy of 7.9999, a correlation coefficient of 0.0001, 98% plaintext recovery after 30% noise corruption, and 96% recovery after 25% ciphertext clipping, demonstrating its robustness for secure medical data transmission.
The rapid growth of IoT-enabled healthcare systems and cloud storage technologies has significantly increased the reliance on digital medical imaging for diagnosis and treatment. However, the storage and transmission of medical images over cloud platforms introduce serious security and privacy concerns, particularly due to cyberattacks, data breaches, and adversarial manipulations. Medical images, including MRI, CT, and X-ray scans, contain sensitive patient information that must be protected during storage and communication. Traditional cryptographic techniques, while effective, are increasingly vulnerable to emerging threats, especially with the advent of quantum computing. Recent advances in Artificial Intelligence (AI) and Quantum Neural Networks (QNNs) have opened new possibilities for secure medical image cryptanalysis and protection. Deep learning-based cryptographic techniques have demonstrated the ability to enhance image encryption, authentication, and secure transmission while maintaining diagnostic quality. Additionally, quantum computing introduces advanced cryptographic paradigms such as Quantum Key Distribution (QKD) and quantum-enhanced encryption, which significantly improve data security in IoT-enabled cloud environments. Quantum neural networks further enhance cryptographic systems by enabling efficient processing of high-dimensional medical image data while ensuring privacy preservation through quantum cryptography mechanisms. Moreover, recent studies highlight that adversarial attacks on AI-based medical imaging systems can lead to incorrect diagnoses, emphasizing the need for robust and secure architectures. This paper presents a comprehensive systematic review of recent advances in secure medical image cryptanalysis using quantum neural networks in IoT-enabled cloud storage systems. It examines AI-driven cryptographic techniques, quantum-based security models, and hybrid frameworks, while identifying research trends, challenges, and future directions for developing secure and efficient healthcare systems.