The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.
The rapid growth of Internet of Things (IoT)-enabled healthcare systems has led to the widespread use of cloud-based platforms for storing and transmitting medical images. While this paradigm improves accessibility and scalability, it also introduces significant security challenges, including unauthorized access, data breaches, and cyberattacks. Secure medical image cryptanalysis has therefore become a critical research area, focusing on evaluating and strengthening encryption techniques used in healthcare systems. Recent advancements in artificial intelligence, particularly deep learning and quantum neural networks (QNNs), have opened new avenues for enhancing medical image security. Quantum neural networks leverage principles of quantum computing such as superposition and entanglement to improve computational efficiency and encryption strength. Additionally, deep learning-based cryptographic techniques enable intelligent key generation, anomaly detection, and attack mitigation. This paper presents a comprehensive review of secure medical image cryptanalysis techniques using quantum neural networks in IoT-enabled cloud storage environments. The study explores hybrid approaches combining quantum-inspired algorithms, deep learning models, and cryptographic techniques to improve data confidentiality, integrity, and availability. Recent research highlights the effectiveness of quantum-enhanced encryption frameworks, such as quantum key distribution and hybrid cryptographic systems, in protecting sensitive medical data. Despite these advancements, challenges such as high computational complexity, limited quantum hardware availability, and scalability issues persist. This review analyses recent developments, identifies research gaps, and provides future directions for developing secure and efficient medical image cryptanalysis systems.
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.
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.
Quantum image encryption has become a promising direction to protect visual information stored or transmitted through cyberspace due to its cryptographic power and resilience that are under-researched. This survey makes a systematic study of the current advancement in the field of quan-tum image encryption concerning cryptographic security and AI-based approaches. We survey the state of quantum-based encryption, such as quantum key distribution, chaotic quantum systems, and AI methods, such as deep learning, to generate keys and detect attacks through a thorough literature review. The significant findings include that quantum encryption may provide unprecedented security levels through mechanisms such as superposition and entanglement, yet computational complexity and hardware constraints are obstacles to implementation. AI-based methods have improved efficiency and adaptability but have created adversarial attack risks. We can conclude that quantum image encryption has the potential to transform the secure transmission of data, but it is necessary to incorporate AI-powered optimizations into the proven quantum systems to ensure scalability and resistance to evolving threats. Hybrid models are the way of the future as they need to find a balance between security and realistic usage.
The rapid growth of digital healthcare systems and connected medical devices has amplified the need for advanced data protection strategies. Traditional security methods face growing challenges from increasing computational power and emerging threats, including the potential impact of quantum computing. This chapter explores the integration of quantum-inspired security and neuromorphic computing as a novel approach for safeguarding sensitive medical data. Quantum-inspired techniques provide strong, future-resistant encryption by leveraging principles such as quantum key distribution and quantum-randomized algorithms, ensuring secure storage, transmission, and access control of patient information. Neuromorphic computing, modelled after the human brain, offers energy-efficient, adaptive, and parallel processing capabilities, making it ideal for edge devices like wearable sensors and Internet of Things enabled medical equipment. By combining these two technologies, healthcare systems can achieve a hybrid framework that enables quantum-resilient encryption, real-time anomaly detection, and adaptive threat monitoring, all with minimal latency and power consumption. This approach not only strengthens patient data privacy and regulatory compliance but also lays the foundation for scalable, intelligent, and secure medical data infrastructures. The chapter further discusses implementation strategies, potential applications, and future directions, highlighting the transformative potential of these emerging technologies in shaping next-generation healthcare security solutions.
The increasing digitalization of the healthcare system, driven by electronic health records, medical IoT devices, and AI-aided diagnostics, has significantly expanded the attack surface for cyber threats. Although current cryptographic protocols provide sufficient security against classical adversaries, new developments in quantum computing threaten the confidentiality, integrity, and availability of medical information as never before. Shor’s algorithm could break widely used asymmetric encryption systems, and Grover’s algorithm weakens symmetric ones, which could render modern healthcare security systems obsolete. This review provides an in-depth and organized analysis of the evolving quantum threat environment in healthcare ecosystems and of the need to move toward quantum-resilient security architectures. It synthesizes the existing literature on post-quantum cryptography (PQC) and identifies lattice-based, hash-based, multivariate, and code-based algorithms as potentially viable long-term replacements. Moreover, it examines the cybersecurity issues that quantum-enabled attacks pose to medical IoT systems, clinical decision-support systems, wearable sensors, telemedicine systems, and cloud-based health information exchanges. The review also covers hybrid quantum-classical defenses, quantum key distribution (QKD), quantum random number generation (QRNG), and blockchain-based trust systems that aim to make systems resilient to advanced persistent quantum attacks. This paper identifies key gaps and outlines future research directions for developing quantum-secure medical systems. The results highlight the importance of early adoption of PQC standards and a layered cyber defense approach to protect patient privacy, ensure regulatory compliance, and retain trust in next-generation digital medical data communication.
Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners—This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behavior of real-life users in healthcare processes.