The security of data communication in Smart Grids (SGs) is traditionally reliant on cryptosystems based on the computational difficulty of certain mathematical problems. However, the advent of quantum computers, with their unparalleled computational power, threatens the robustness of classical cryptographic methods. Despite significant research into quantum-secure solutions, limited attention has been given to quantum-based security in SGs. To address these emerging security challenges, this paper proposes a quantum-based multifaceted secure communication model (Qu-MCM) for SGs, offering unconditional security by leveraging quantum mechanical principles. The key contributions include: quantum encryption of power consumption data using truly random keys, a quantum-secure key exchange protocol between power users and utility suppliers, and data authentication and integrity verification through quantum fingerprinting. The model is experimentally evaluated via simulations on classical computer and actual quantum hardware using the IBM Qiskit platform. The experimental results show that the encryption algorithm achieves an accuracy of 81.73%, while the authentication algorithm reaches 87.4% through a probability distribution graph where accuracy signifies the dominance of correct outputs. The results demonstrate the validity of the proposed model, with a comprehensive security analysis confirming its perfect security, where perfect security is defined as the maximally mixed states achieved after encryption that is impossible to break for an eavesdropper equipped with quantum resources.

The intermittent communication of power usage information in smart grids (SGs) is highly susceptible to potential cyberattacks, which is protected by classical public-key cryptosystems. However, recent advancements in the field of quantum computing have rendered these cryptosystems trivially insecure, triggering the immediate requirement for a highly resilient data communication scheme. This paper addresses the above-mentioned issue by designing a Quantum Encryption and Quantum Fourier Transformation (QFT) based secure data communication and aggregation model (QC-EAM) for SG environments. QC-EAM enables the power consumption data of the users to be encrypted using truly random encryption keys, ensuring unconditional security, while group homomorphism of Clifford Gates is utilized to achieve homomorphic evaluation of the encrypted quantum states. Moreover, a quantum-based data aggregation circuit is developed that performs QFT over the encrypted quantum states, yielding a secure and aggregated state. Furthermore, entanglement swaps-based quantum networking is considered as an enabling infrastructure for the proposed scenario, ensuring preservation of highly susceptible quantum states. The experimental evaluation of QC-EAM is achieved through classical computer-based simulations and tested on actual quantum system, provisioned through the IBM Qiskit platform. Statistical measurements of quantum circuits yield significantly accurate results, proving the validity of QC-EAM. Moreover, a thorough security analysis is also provided that confirms the perfect security achieved through maximally mixed states.

The rapid growth of geographically distributed cloud data centers has intensified the demand for secure, privacy-preserving, and resource-efficient collaboration among mutually untrusted cloud environments. This paper presents BlockFed, a blockchain-empowered federated learning framework designed to enable cyber-threat-resilient collaboration across geographically distributed data centers. In BlockFed, each data center independently trains local models using private workload data and shares only the computed gradients rather than raw data, with a centralized aggregation server through a secure blockchain layer. A Proof-of-Work consensus mechanism is employed to validate gradient transactions and maintain an immutable, tamper-resistant ledger, ensuring trust and integrity among participating entities. The centralized server aggregates blockchain-verified updates to construct a global model, which is iteratively redistributed to support collaborative learning. This integrated learning process enhances task-level resource demand prediction while simultaneously improving system security and operational efficiency across all participating data centers. Extensive simulations on the Google Cluster Dataset show that BlockFed outperforms state-of-the-art baselines, including ISTM, ETP-WE, First-Fit, Best-Fit, and Random-Fit, in resource utilization, power consumption, and active server reduction. BlockFed achieves up to 7-81% higher resource utilization, 41-58% lower power consumption, and 31-65% fewer active servers, while attaining a cyber-threat estimation accuracy of 93.19%.