Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning - Utomo, Wiranto Herry; Rosalina, Rosalina; Afriyadi, Afriyadi (Institute of Advanced Engineering and Science, 2024)
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.