Research on Privacy Protection and Data Sharing Mechanism for Digital Environment: Based on Reinforcement Learning
Dong Li, Feng Gao, Huiping Meng, Yong Hyeon Yang, Peilin Cai, Jianhui Xu, Jian Yang, Mengjiao Wang · 2024
Abstract: The digital environment is highly dynamic and constantly changing, and traditional privacy protection and data sharing mechanisms are too fixed to adapt to the rapid changes in the environment. Therefore, this article optimizes privacy protection and data sharing mechanisms for digital environments based on reinforcement learning. The first step of optimization is to collect and analyze historical data on user behavior, and implement strategy initialization. The second step is to construct an environmental model and design a reward mechanism to balance the success rate of privacy protection and the effectiveness of data sharing. The third step is to adopt a deep Q-network to improve the stability and efficiency of mechanism learning. Finally, an experimental evaluation will be conducted on the mechanism based on reinforcement learning. The results indicate that the privacy leakage risk of privacy protection and data sharing mechanisms optimized based on reinforcement learning decreases with the increase of iteration times, and the computational complexity is generally below 6%. This demonstrates the potential of reinforcement learning in this field and provides valuable references for practical applications.