Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning

Abstract

The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-preserving strategies based on the evolving data environment and user requirements, while ensuring compliance with the latest security and privacy standards such as ISO/IEC 27001:2023. Experimental results demonstrate significant improvements in both privacy protection and data utility, surpassing traditional PPDM methods. Our findings highlight the potential of DRL in enhancing privacy-preserving mechanisms, offering a scalable and efficient solution for secure big data analytics.