AI-Powered Quantum-Resistant Blockchain for Secure Financial Transactions: A Privacy-Preserving Approach
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Abstract
The emergence of quantum computing presents a critical challenge to the security of blockchain-based financial transactions, central bank digital currencies (CBDCs), and decentralized finance (DeFi). Traditional cryptographic approaches, such as Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA encryption, are at risk due to quantum algorithms capable of breaking them efficiently. This study introduces a novel AI-powered blockchain framework designed to be resilient against quantum-based attacks, integrating lattice-based cryptography and post-quantum secure hash functions to safeguard financial transactions. To further enhance security and privacy, the model employs federated learning and homomorphic encryption, ensuring compliance with regulatory standards while mitigating risks associated with data exposure. Additionally, a hybrid AI-optimized Byzantine Fault Tolerant (BFT) consensus mechanism is proposed to address scalability and efficiency limitations in blockchain networks. Experimental evaluations indicate that the proposed system achieves a 96% fraud detection accuracy, improves blockchain scalability by 40%, and lowers computational overhead by 35%, outperforming conventional blockchain security techniques. These findings highlight the potential of AI-driven, quantum-resistant blockchain models in securing digital financial ecosystems and align with ongoing global efforts to reinforce financial cybersecurity against quantum threats.
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