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SN Computer Science

Secure Cloud Data Access: Unifying Quantum Key Distribution and Attribute-Based Encryption for Enhanced Data Protection

Ashutosh Kumar, Garima Verma

Published Oct 16, 2023• 9 citations
Publisher
DOI
10.1007/s42979-023-02285-z

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Data security challenges in cloud computing have grown as a fundamental issue because of rising cyber threats during this period. The proposed model QC-CSM relies on the quantum key distribution process (QKDP) and ABE to use quantum cryptography in developing a cloud security framework that enhances security measures. The model provides key distribution together with authentication and encryption security through the application of the quantum no-cloning theorem alongside quantum mechanics principles. The experimental results conducted through CloudSim and iQuantum generate data that confirms the model exhibits efficient performance across storage, encryption, and processing speed. The key generation process takes 35% less time than conventional multi-authority attribute-based encryption (MAABE) and pairing-based provable multi-copy data possession (PB-PMDP) encryption methods. Additionally, the encryption and decryption operations operate at optimized speeds. The reduction of storage overhead (SO) amounts to 20% which leads to improved memory performance. The model demonstrates effective detection abilities to identify quantum key distribution (QKD) network eavesdropping which prevents secure network breaches. The experimental data demonstrate that QC-CSM delivers both a secure platform and a scalable and efficient cloud security infrastructure. The chapter shows that quantum cryptography enables cloud system protection when it incorporates QKD components with contemporary encryption technologies.
The energy and power industry faces increasing data security challenges amidst its digital transformation. Databases, as the foundation of digital infrastructure, require advanced encryption techniques to safeguard sensitive information. This paper proposes a novel encryption framework, QKD-ABE, that integrates Quantum Key Distribution (QKD) with Attribute-Based Encryption (ABE) to enhance the confidentiality, integrity, and access control of power system data. QKD enables unconditional security in key distribution by leveraging quantum mechanics, effectively preventing key interception and tampering. ABE provides fine-grained access control by enforcing encryption policies based on user attributes, ensuring that only authorized individuals can access private data. The proposed scheme also demonstrates strong resistance to common security threats, including man-in-the-middle and eavesdropping attacks, through quantum measurement disturbance and Quantum Bit Error Rate (QBerr) verification. Experimental results and theoretical analysis indicate that QKD-ABE offers a secure, efficient, and scalable solution for protecting sensitive data in modern smart grid environments.
Nowadays, succeeding safe communication and protection-sensitive data from unauthorized access above public networks are the main worries in cloud servers. Hence, to secure both data and keys ensuring secured data storage and access, our proposed work designs a Novel Quantum Key Distribution (QKD) relying upon a non-commutative encryption framework. It makes use of a Novel Quantum Key Distribution approach, which guarantees high level secured data transmission. Along with this, a shared secret is generated using Diffie Hellman (DH) to certify secured key generation at reduced time complexity. Moreover, a non-commutative approach is used, which effectively allows the users to store and access the encrypted data into the cloud server. Also, to prevent data loss or corruption caused by the insiders in the cloud, Optimized Genetic Algorithm (OGA) is utilized, which effectively recovers the data and retrieve it if the missed data without loss. It is then followed with the decryption process as if requested by the user. Thus our proposed framework ensures authentication and paves way for secure data access, with enhanced performance and reduced complexities experienced with the prior works.