Cloud computing has become the backbone of today's high-performance computing systems, enabling the storage, processing, and delivery of large-scale data across heterogeneous networks. However, the increasing reliance on cloud infrastructures raises important questions regarding data privacy and confidentiality, as well as efficient resource utilization. Traditional forms of encryption often struggle to find the right balance between sufficient security and efficiency in processing, particularly in dynamic cloud environments in which adaptive resource allocation is necessary. To bridge existing gaps, this work proposes a new Quantum-Adaptive Neural Encryption Algorithm (Q-ANEA), a framework that can sustain privacy of data in a high-performance cloud environment, by proposing quantum-inspired and neurocryptographic principles to combat the potentially dangerous, dynamic cloud configurations. The Q-ANEA uses the probabilistic nature of quantum operations to enhance encryption semantic, while a trained neural network can adapt to varying data patterns and loads, reducing latency and computational overhead. Benchmarks on cloud datasets were used in experiments that evaluated Q-ANEA against traditional forms of encryption (i.e., AES and RSA) with speed of encryption, integrity, and resilience to cyber demand from nefarious actors in mind. The findings show that Q-ANEA is as much as 35% faster than the fastest encryption in terms of performance, has 99.8% data integrity, and is more robust to classical and quantum attack simulations. The work further demonstrates that techniques that are quantum-inspired (quantum algorithm-based) when combined with learning algorithms (adaptive learning algorithms) can be a potentially great building block for secure, scalable, quantum powered cloud systems. In summary, the work proposes Q-ANEA as a promising option to develop a privacy-aware cloud computing system that remains high in terms of security and does not sacrifice computational efficiency, while laying the foundation to further integrate intelligent encryption deployment mechanisms for the next generation of cloud based systems.
Cloud computing has become the backbone of today's high-performance computing systems, enabling the storage, processing, and delivery of large-scale data across heterogeneous networks. However, the increasing reliance on cloud infrastructures raises important questions regarding data privacy and confidentiality, as well as efficient resource utilization. Traditional forms of encryption often struggle to find the right balance between sufficient security and efficiency in processing, particularly in dynamic cloud environments in which adaptive resource allocation is necessary. To bridge existing gaps, this work proposes a new Quantum-Adaptive Neural Encryption Algorithm (Q-ANEA), a framework that can sustain privacy of data in a high-performance cloud environment, by proposing quantum-inspired and neurocryptographic principles to combat the potentially dangerous, dynamic cloud configurations. The Q-ANEA uses the probabilistic nature of quantum operations to enhance encryption semantic, while a trained neural network can adapt to varying data patterns and loads, reducing latency and computational overhead. Benchmarks on cloud datasets were used in experiments that evaluated Q-ANEA against traditional forms of encryption (i.e., AES and RSA) with speed of encryption, integrity, and resilience to cyber demand from nefarious actors in mind. The findings show that Q-ANEA is as much as 35% faster than the fastest encryption in terms of performance, has 99.8% data integrity, and is more robust to classical and quantum attack simulations. The work further demonstrates that techniques that are quantum-inspired (quantum algorithm-based) when combined with learning algorithms (adaptive learning algorithms) can be a potentially great building block for secure, scalable, quantum powered cloud systems. In summary, the work proposes Q-ANEA as a promising option to develop a privacy-aware cloud computing system that remains high in terms of security and does not sacrifice computational efficiency, while laying the foundation to further integrate intelligent encryption deployment mechanisms for the next generation of cloud based systems.
The rapid adoption of cloud computing has introduced significant concerns regarding data privacy and security, particularly in the face of evolving cryptographic threats, including those from future quantum computers. Traditional encryption standards, such as AES and RSA, may become vulnerable to quantum attacks, notably through Shor?s and Grover?s algorithms. This research proposes a novel Quantum-Inspired Encryption Mechanism (QIEM) designed to enhance cloud data protection by integrating principles from quantum mechanics-such as superposition, entanglement, and no-cloning-into a classical cryptographic framework. The proposed mechanism employs a hybrid approach combining post-quantum cryptographic algorithms with quantum-inspired key distribution and dynamic encryption layers. Simulation results demonstrate that QIEM offers robust resistance against both classical and quantum attacks while maintaining efficient performance in cloud environments. This study provides a forward-looking solution to safeguard sensitive data in the cloud against current and future threats.
The exponential growth of data-driven systems has outpaced the computational capacity of conventional cloud infrastructures, demanding new paradigms of intelligence and optimization. This research introduces a quantum-enabled cloud intelligence framework that integrates post-classical optimization methods with data-centric artificial intelligence pipelines. The study investigates how quantum-inspired and hybrid quantum–classical models can enhance large-scale optimization, feature extraction, and decision processes within distributed cloud environments. By coupling quantum approximate optimization algorithms (QAOA) and variational quantum circuits (VQC) with classical data analytics layers, the framework achieves reduced computational latency, improved convergence for high-dimensional problems, and enhanced interpretability of AI-driven outcomes. A prototype quantum-cloud orchestration layer is developed to dynamically allocate quantum resources through cloud APIs, enabling adaptive workload balancing across heterogeneous architectures. Empirical results from simulation and hybrid execution demonstrate significant performance gains over classical baselines in terms of both efficiency and solution quality. Beyond performance metrics, the study emphasizes ethical and sustainable computing principles, ensuring that quantum acceleration aligns with transparent, explainable, and accountable AI operations. The outcomes position quantum-enabled cloud intelligence as a foundational technology for the next generation of data-centric optimization and cognitive automation.
The rise of cloud computing has transformed data storage and processing but introduced new vulnerabilities, especially with the impending threat of quantum computing. Traditional cryptographic methods, though currently effective, are at risk of being compromised by quantum attacks. This research aims to develop a quantum-resistant security framework for cloud environments, combining lattice-based cryptography with Quantum Key Distribution (QKD) protocols, particularly the E91 protocol, for secure key management. The framework also incorporates quantum authentication protocols to enhance user identity verification, protecting against unauthorized access and tampering. The proposed solution balances robust security with practical implementation, ensuring scalability and efficiency in real-world cloud environments. Performance evaluations indicate an encryption time of approximately 30 milliseconds, outperforming existing methods such as RSA and DES. This research contributes to the development of future-proof cryptographic standards, addressing both current security challenges and emerging quantum computing threats. By leveraging quantum mechanics, the framework strengthens cloud-based data protection, providing a resilient solution against evolving cyber risks. The results hold significant promise for advancing cloud security, laying the groundwork for next-generation encryption techniques that can withstand the threats posed by quantum computing.
As the digital landscape evolves, the reliance on cloud computing for critical infrastructure across various sectors highlights the need for robust security mechanisms to protect sensitive data from emerging cyber threats, particularly those posed by quantum computing. Traditional cryptographic systems, while currently effective, are vulnerable to quantum attacks, necessitating the development of quantum-resistant solutions. This research introduces the Quantum-Resilient Cryptographic Framework (QRCF), a hybrid, adaptive cryptographic framework designed to safeguard cloud environments against both classical and quantum threats. The QRCF integrates lattice-based Kyber and code-based McEliece algorithms, offering a comprehensive and scalable solution for secure data storage, management, and transmission. Key contributions include the development of a dynamic security management layer that adapts to real-time threat analysis, ensuring continuous protection against evolving threats, and the implementation of robust post-quantum cryptographic methods that maintain high performance and low computational overhead. Quantitative analysis shows that the QRCF maintains a high throughput of 420 MB/s for encryption and 400 MB/s for decryption under normal operations, with latency as low as 3.2 ms and 3.6 ms, respectively. The framework exhibited strong resistance to various attack models, with success rates of 1.0% for brute-force attacks, 1.875% for MITM attacks, 0.833% for side-channel attacks, and 1.6% for replay attacks. Against quantum threats, the QRCF showed no vulnerability to Shor’s Algorithm and a minimal success rate of 0.667% for Grover’s Algorithm. The framework’s design ensures seamless integration with existing cloud infrastructures, providing practical migration strategies to quantum-safe cryptography without disrupting operational workflows. By addressing key research gaps in quantum-safe cloud security, this study contributes significantly to the field, offering a robust, scalable, and efficient cryptographic solution that enhances the security and operational performance of cloud environments in the face of advancing quantum computational capabilities.
The high growth rate of cloud storage companies has reshaped the digital management of data among individuals and companies. Nevertheless, the future of quantum computing spells a major danger to classical cryptographic algorithms like the RSA and the ECC which forms the backbone to the current cloud security infrastructures. In order to mitigate this new risk, this paper will present a new Post-Quantum Cryptography (PQC)-based security architecture, customized to cloud storage application. The model combines lattice-based key encapsulation models and hash-based authentication models to provide long-term data confidentiality, integrity, and non-repudiation. The basic uniqueness of the offered model is its hybrid cryptographic implementation, which will have quantum-resilient key exchange, and a hardened hash-based signature verification as a single workflow of cloud security. This assimilation improves resistance to quantum-based attacks and retains viable computational efficiency. The evaluation of performance shows that the framework can be used in large-scale and distributed cloud systems since it has a well-balanced trade-off between high quantum resilience and controllable processing overheads. The suggested PQC scheme will create moderate overhead and high future-proof security, in comparison to the traditional crypto-systems. The paper portrays the strategic value of shifting towards quantum-resistant encryption systems to guarantee long-term data security in the post-quantum future.
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.