Hybrid Quantum Computing and Decision Tree-Based Data Mining for Improved Data Security

Abstract

Data mining and quantum computing have become potent methods for analyzing massive datasets and gaining insightful conclusions. However, conventional techniques for data mining are susceptible to quantum attacks, necessitating the development of quantum-resistant methods like lattice-based cryptography. This research provides a hybrid strategy for better data analysis and security that blends quantum computing with decision tree-based data mining. The suggested algorithm was tested against various datasets, and the findings indicated that it provided substantial protection against quantum assaults while achieving great accuracy and precision in data analysis. On the datasets tested, the suggested algorithm specifically attained an average precision of 95.3% and an accuracy of 96.5%. Additionally, the algorithm resisted quantum attacks, maintaining accuracy and precision subjected to the most extreme quantum attacks. Overall, the evaluation findings show how effective and efficient the suggested hybrid technique is for data analysis and protection, providing a workable answer for practical post-quantum applications.