A Quantum‐Resilient and Adaptive Privacy Framework With Anonymization Techniques for Secure Data Mining Operations

ABSTRACT The increasing reliance on data mining for extracting valuable insights has raised significant concerns about data security and privacy, especially with evolving cyber threats and the advent of quantum computing. Traditional security frameworks often depend on static encryption mechanisms that are vulnerable to quantum attacks and lack adaptability to emerging threats. To address these limitations, this research introduces the Quantum‐Resilient and Adaptive Privacy (QRAP) framework, which integrates Federated Learning (FL), Quantum‐Resistant Cryptography (QRC), and Anonymization Techniques to enhance security, privacy, and efficiency in data mining operations. Quantum‐Resilient refers to the framework's integration of cryptographic protocols that remain secure under known quantum algorithms (e.g., Grover's and Shor's), particularly using post‐quantum signature schemes and secure key exchange mechanisms. QRAP dynamically updates cryptographic protocols, ensuring resilience against evolving cyber threats, while FL enables secure, decentralized data collaboration without exposing raw data. Additionally, advanced Anonymization Techniques such as Differential Privacy, k‐Anonymity, l‐Diversity, and t‐Closeness further enhance privacy by preventing sensitive data from being re‐identified, ensuring compliance with stringent data protection regulations. Compared to traditional encryption‐based frameworks, QRAP offers a dynamic and quantum‐resilient approach, effectively mitigating vulnerabilities in centralized data mining operations. Experimental results demonstrate that QRAP achieves a 35% enhancement in privacy preservation and a 28% reduction in computational overhead, outperforming existing solutions in protecting sensitive information within dynamic data environments. These findings underscore QRAP's potential to revolutionize secure data mining by providing a scalable, privacy‐preserving, and future‐proof framework. Future research will focus on real‐time optimization of QRAP and its integration with blockchain for enhanced transparency, security, and trust in decentralized data operations.