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Quantum Information Processing

Q-ECS: quantum-enhanced cloud security with attribute-based cryptography and quantum key distribution

Umer Nauman, Miaolei Deng, Yuhong Zhang, Sheheryar Khan, Uzair Salman

Published Jun 10, 2025• 8 citations
Publisher

Abstract

Abstract In the contemporary landscape of advanced technological ecosystems, the ubiquity of cloud computing serves as a cornerstone for orchestrating and maintaining a myriad of global datasets, information repositories, and service infrastructures. This widespread adoption of cloud computing, however, has brought forth a pressing need to confront and mitigate security challenges, necessitating the formulation of resilient protocols to safeguard the sanctity and confidentiality of data. In response to this imperative, our research endeavors to propose an innovative and secure paradigm for cloud data access, harnessing the synergies of attribute-based cryptography (ABC) and quantum key distribution (QKD). This methodology capitalizes on the intrinsic security of quantum channels to facilitate the secure transmission of cryptographic keys across nodes. We validate the effectiveness of our model through extensive simulations. This comparison, which is based on state-of-the-art methods, looks at a wide range of performance metrics, such as the time it takes to create a key, the time it takes to decrypt and encrypt, and other important factors. Compared to state-of-the-art algorithms, our proposed algorithm performs better with better encoding times (2.6s to 5.2s) and decryption timings (1.2s to 3.6s). While QKD-ABC regularly obtains the shortest analysis times (29.1s to 149.1s), it is clear that QKD-ABC is the best method for safe cloud data access. The particular numerical metrics demonstrate the unmatched performance of QKD-ABC in a variety of activities.

DOI
10.1007/s11128-025-04786-7

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Abstract In the contemporary landscape of advanced technological ecosystems, the ubiquity of cloud computing serves as a cornerstone for orchestrating and maintaining a myriad of global datasets, information repositories, and service infrastructures. This widespread adoption of cloud computing, however, has brought forth a pressing need to confront and mitigate security challenges, necessitating the formulation of resilient protocols to safeguard the sanctity and confidentiality of data. In response to this imperative, our research endeavors to propose an innovative and secure paradigm for cloud data access, harnessing the synergies of attribute-based cryptography (ABC) and quantum key distribution (QKD). This methodology capitalizes on the intrinsic security of quantum channels to facilitate the secure transmission of cryptographic keys across nodes. We validate the effectiveness of our model through extensive simulations. This comparison, which is based on state-of-the-art methods, looks at a wide range of performance metrics, such as the time it takes to create a key, the time it takes to decrypt and encrypt, and other important factors. Compared to state-of-the-art algorithms, our proposed algorithm performs better with better encoding times (2.6s to 5.2s) and decryption timings (1.2s to 3.6s). While QKD-ABC regularly obtains the shortest analysis times (29.1s to 149.1s), it is clear that QKD-ABC is the best method for safe cloud data access. The particular numerical metrics demonstrate the unmatched performance of QKD-ABC in a variety of activities.
The widespread cryptographic methods RSA and AES have become more susceptible to quantum-based attacks because researchers developed quantum algorithms such as Shor's and Grover's. QKD chromosomes from quantum mechanics grant authorized parties the ability to exchange cryptographic keys securely alongside built-in eavesdropping detection functionality. In the proposed method a Quantum Key Distribution (QKD) is used to integrate into cloud environments to secure data against quantum threats which have become crucial security concerns today. A new security framework for combining QKD with standard cloud utilities strengthens both access privacy and verify data integrity and ensure authentication of user information. The system framework consists of key generation from quantum elements followed by distribution through quantum channels to support secure communication protocols for Software as a Service (SaaS) and Platform as a Service (PaaS) as well as Infrastructure as a Service (IaaS). The key performance evaluation of the QKD system includes measurements of key generation rate together with Quantum Bit Error Rate (QBER) and eavesdropping detection accuracy rates. The analysis against traditional cryptographic systems proves that QKD stands superior in quantum attack resilience. The proposed system demonstrates its efficiency and robustness through simulation outcomes as well as visual representations. The presented work demonstrates both the revolutionary capability of quantum technology for cloud security along with a deployable system architecture for future systems implementation. The research work advances the creation of cloud infrastructure systems which combine high security resilience to protect against future cyber threats.
Recent developments in quantum-enhanced security have demonstrated encouraging promise for enhancing cloud computing environments' security. Utilizing quantum physics, in particular Quantum Key Distribution (QKD), provides a new method for generating cryptographic keys and improves cloud data transport security. The present study offers a thorough investigation of the integration of QKD with conventional encryption techniques, including Advanced Encryption Standard (AES), in order to tackle the dynamic cyber security scenario in cloud computing. The approach entails combining AES for encryption and decryption procedures and establishing a QKD layer within the cloud architecture to produce true quantum keys utilizing Quantum in Cloud technology. Data transmission security is greatly improved by the smooth integration of AES with QKD-generated keys, guaranteeing confidentiality, integrity, and authenticity. In addition, strong key management practices are put in place to handle cryptographic keys safely at every stage of their lifespan, reducing the possibility of unwanted access or interception. The suggested approach successfully addresses the difficulties presented by cyber threats by offering a robust and flexible means of enhancing security in cloud-based systems. Using both traditional and quantum encryption methods, this strategy provides a strong barrier against cyber-attacks, data leaks, and other security flaws. After 70 simulation rounds, the suggested strategy, which is implemented using the QKD-AES framework in Python software, achieved a data access rate of 820 MB/s. In addition to providing an accurate and quantitative assessment of the performance, this also exhibits a high data access rate attained under simulated conditions. At 15 milliseconds, the key generation time was achieved with efficiency, guaranteeing the quick creation of secure cryptographic keys in cloud environments. Overall, there is a lot of potential in using quantum-enhanced security techniques to protect sensitive data and guarantee the integrity of cloud computing infrastructures.
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.
With the growing adoption of cloud computing, ensuring data security in cloud environments has become a critical concern for business organizations. Quantum cryptography utilizes the principles of quantum mechanics to guarantee secure communication, as any attempt to eavesdrop will change the quantum states, alerting the parties of the intrusion. This paper proposes a multi-qubit Quantum Key Distribution (QKD) ciphertext-policy attribute-based encryption (CP-ABE) for cloud security. The proposed multi-qubit QKD model for secure cloud data using quantum cryptography involves the use of a quantum key distribution protocol to generate a secure key for encryption and decryption. This protocol involves sending quantum signals through a quantum channel to distribute a secret key between the sender and the receiver. The key is then used for the encryption and decryption of data using the CP-ABE technique. This technique allows the encryption and decryption of data based on attributes rather than an explicit key exchange, making it particularly suitable for cloud environments where data is stored and processed by multiple users with varying levels of access. The positive results from the proposed simulation model suggest the potential of quantum cryptography in securing cloud data.
The integration of quantum cryptography and blockchain offers a transformative approach to enhance data security in cloud environments. Quantum cryptography, leveraging principles of quantum mechanics such as quantum key distribution (QKD), ensures unbreakable encryption by enabling secure key exchange. Simultaneously, blockchain technology introduces decentralization, transparency, and immutability, ensuring data integrity and preventing unauthorized tampering. This research explores the synergy between these technologies to develop a novel framework for cloud security. The proposed model leverages QKD for secure communication channels and blockchain-based distributed ledgers for verifying transactions and access control. Key contributions include a hybrid encryption mechanism using quantum keys, a consensus algorithm optimized for cloud operations, and integration strategies to mitigate quantum attacks. Performance evaluations demonstrate enhanced resilience against data breaches, improved key management, and seamless scalability for modern cloud architectures. This study establishes a secure, future-proof infrastructure that addresses emerging threats in the post-quantum era while ensuring trust, confidentiality, and data integrity in cloud ecosystems.
Summary Improvement of privacy and security in data centers is challenging with proficient safety key managing. So as to resolve this issue, data centers require proficient quantum cryptographic techniques with authentication methods that are suitable to improve the privacy and security with lesser intricacy. In addition, quantum cryptography (QC) offers maximal security with lesser complication, which raises the security strength and storing capability of big data. This work intends to introduce a QC oriented data security model in cloud via electing the optimal private key. Here, attribute based encryption (ABE) is performed for ensuring better data transmission among the nodes in cloud. For key generation in ABE, improved QKD is adopted in this work. Depending upon this, the decryption and encryption is done. Moreover, for optimal secret key selection in ABE, self‐modified Aquila optimization (SM‐AO) scheme is deployed. Further, analysis is done depending upon varied metrics.