In the era of ubiquitous big data, ensuring secure storage, transmission, and processing has become a paramount concern. Classical cryptographic methods face increasing vulnerabilities in the face of quantum computing advancements. This research proposes an enhanced big data security framework integrating a quantum chaotic map of key sequence (QCMKS), which synergizes the principles of quantum mechanics and chaos theory to generate highly unpredictable and non-repetitive key sequences. The system incorporates quantum random number generation (QRNG) for true entropy sources, quantum key distribution (QKD) for secure key exchange immune to eavesdropping, and quantum error correction (QEC) to maintain integrity against quantum noise. Additionally, quantum optical elements transformation (QOET) is employed to implement state transformations on photonic qubits, ensuring robustness during transmission across quantum networks. The integration of QCMKS with QRNG, QKD, QEC, and QOET significantly enhances the confidentiality, integrity, and availability of big data systems, laying the groundwork for a quantum-resilient data security paradigm. While the proposed framework demonstrates strong theoretical potential for improving big data security, its practical robustness and performance are subject to current quantum hardware limitations, noise sensitivity, and integration complexities.
In the era of ubiquitous big data, ensuring secure storage, transmission, and processing has become a paramount concern. Classical cryptographic methods face increasing vulnerabilities in the face of quantum computing advancements. This research proposes an enhanced big data security framework integrating a quantum chaotic map of key sequence (QCMKS), which synergizes the principles of quantum mechanics and chaos theory to generate highly unpredictable and non-repetitive key sequences. The system incorporates quantum random number generation (QRNG) for true entropy sources, quantum key distribution (QKD) for secure key exchange immune to eavesdropping, and quantum error correction (QEC) to maintain integrity against quantum noise. Additionally, quantum optical elements transformation (QOET) is employed to implement state transformations on photonic qubits, ensuring robustness during transmission across quantum networks. The integration of QCMKS with QRNG, QKD, QEC, and QOET significantly enhances the confidentiality, integrity, and availability of big data systems, laying the groundwork for a quantum-resilient data security paradigm. While the proposed framework demonstrates strong theoretical potential for improving big data security, its practical robustness and performance are subject to current quantum hardware limitations, noise sensitivity, and integration complexities.
From the past few years, the rapid growth in quantum computing and large-scale data systems has created an important need for advanced encryption techniques that withstand quantum-level attacks. However, traditional cryptographic systems are becoming increasingly vulnerable, particularly in big-data environments. To overcome this limitation, this research proposes a secure framework for big data encryption namely Quantum-Chaotic Key-Based Encryption System (QC-KES), which combines quantum randomness and chaos theory to make highly secure encryption keys. Therefore, the methodology begins by producing a quantum random seed utilizing Quantum Random Number Generation (QRNG). It is then enhanced through a chaotic logistic map to generate unpredictable key sequences that are securely shared using Quantum Key Distribution (QKD). Subsequently, quantum optical elements are tuned based on the key sequence to encrypt data at the hardware level. Moreover, Quantum Error Correction (QEC) confirms the signal integrity, while the data are encrypted, transmitted, and later decrypted by the shared key. This system improves key security, accuracy, and resistance to quantum-based threats. Finally, the proposed QC-KES obtained better results in entropy (8.000%) and avalanche effect (52.5 bits) when compared the existing Advanced Encryption Standard (AES) model.
The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of optical communications. This research addresses these challenges by innovatively combining quantum key distribution (QKD), specifically the E91 protocol, with logistic chaotic maps to establish a secure image transmission scheme. Our approach utilizes the unpredictability of chaotic systems alongside the robust security mechanisms inherent in quantum entanglement. The scheme is further fortified with an eavesdropping detection mechanism based on CHSH inequality, thereby enhancing its resilience against unauthorized access. Through quantitative simulations, we demonstrate the effectiveness of this scheme in encrypting images, achieving high entropy and sensitivity to the original images. The results indicate a significant improvement in encryption and decryption efficiency, showcasing the potential of the scheme as a viable solution against the vulnerabilities posed by quantum computing advancements. Our research offers a novel perspective in secure optical communications, blending the principles of chaos theory with QKD to create a more robust cryptographic framework.
The rapid growth in the storage and transmission of visual data across communication, sensing, and decision-making systems requires image encryption methods that are both computationally efficient and resilient against quantum-enabled adversaries. Classical chaotic encryption offers desirable properties such as sensitivity, unpredictability, and large key spaces, but its practical use is limited by restricted chaotic ranges, and conventional confusion–diffusion mechanisms often fail to eliminate structural redundancies in images. This thesis furthers the field by developing novel chaotic and gate-efficient quantum image encryption architectures. First, it introduces a new cosine-modulated polynomial chaotic map and validates its dynamical and statistical suitability for cryptographic key generation. Second, it designs lightweight yet effective chaotic primitives, including the Single-Round Single S-Box (SRSS) confusion mechanism and the featureaware diffusion scheme (PermutEx) that address the limitations in breaking correlation and maintaining efficiency. Third, these components are integrated into a complete endto- end chaos-based encryption framework that combines feature-driven segmentation, adaptive permutation, and dynamic confusion, demonstrating strong security across entropy, correlation, and differential metrics. Finally, the thesis advances two quantum image encryption architectures, i.e., a chaos-enhanced quantum image encryption framework that strengthens quantum confusion and diffusion mechanisms, and a gate-efficient architecture that reduces circuit depth and multi-qubit operations while retaining robust security. The proposed architectures achieve improved computational efficiency compared to existing chaos-based and quantum image encryption techniques, by reducing redundant operations, minimising circuit depth, and enabling lightweight yet effective confusion-diffusion processes. Consequently, this thesis provides a way forward for advancing image encryption by integrating chaotic and quantum approaches, offering practical steps for protecting visual data in the post-quantum era.
In today’s digital landscape, ensuring the security of data transmitted and stored in the cloud is paramount. Traditional encryption methods face challenges in scalability and susceptibility to quantum threats. This abstract presents a novel approach to cloud data security leveraging lightweight chaos encryption and quantum key distribution (QKD). The proposed mechanism combines the dynamic properties of chaos encryption, which offers high entropy and resistance to conventional attacks, with the quantum-level security provided by QKD. By integrating chaos-based encryption techniques with QKD protocols, our system achieves enhanced security for data communications in cloud environments. Furthermore, the lightweight nature of chaos encryption ensures efficient utilization of computational resources, making it suitable for cloud-based applications. This abstract outlines the theoretical framework and operational principles of the proposed security mechanism, highlighting its potential to address the evolving challenges of data security in cloud computing.
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.
Robust security is vital in cloud computing due to the proliferation of sensitive data. This paper presents an innovative quantum-based security framework to address emerging threats. Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) enhances data transmission security by establishing unhackable channels through quantum entanglement, ensuring data integrity. Quantum Homomorphic Encryption (QHE) enables secure computations on encrypted data, ensuring confidentiality during storage. The Quantum Secure Storage and Management Communication (QSSMC) protocol ensures end-to-end privacy and prevents unauthorized access throughout the cloud data lifecycle. Additionally, Quantum Random Number Generation (QRNG) leverages quantum uncertainty to generate truly random numbers, mitigating the risks of pseudo-randomness and strengthening cryptographic applications. By integrating MDI-QKD, QHE, QSSMC, and QRNG, this framework provides robust security, confidentiality, and data integrity. Implemented in Python with a scalability rate of 95%, it offers a comprehensive solution for modern cloud computing challenges.