Research on IoT Data Anonymization Method Based on Multimodal Adversarial Generative Networks and Reinforcement Learning Collaborative Optimization

With the rapid development of the Internet of Things, massive amounts of multi-modal data continue to emerge. Under diverse data types and dynamic environments, traditional anonymization technologies struggle to address privacy leakage risks due to their lack of multi-modal adaptability and static parameter configurations, failing to balance privacy protection and data utility in dynamic IoT environments, and a more efficient and flexible solution is urgently needed. Aiming at the complexity of the coexistence of multi-modal features such as images, texts, and time series in IoT data, We design a generator structure based on a cross-modal attention mechanism to realize deep modelling and privacy risk expression of various modal features and a multi-discriminator collaborative training strategy is introduced to enhance privacy recognition capabilities. Construct a reinforcement learning framework based on a deep deterministic policy gradient and realize the dynamic trade-off between privacy protection and data utility with the adaptive adjustment mechanism of the reward function. In order to realize the effective coupling between multi-modal adversarial generative network and reinforcement learning, it is proposed to use the generator gradient as the policy input to guide the policy update direction and further improve the accuracy and stability of anonymization. In the context of large-scale IoT data processing, combined with a distributed asynchronous training mechanism, the convergence and consistency of the model under multi-node parallel conditions are ensured. In the IoT data anonymization experiment, after using the collaborative optimization method of multi-modal adversarial generation network and reinforcement learning, the data anonymization efficiency increased by 58.7%, and the data loss rate decreased by 45.3%. The privacy leakage rate in the test set containing 236,000 samples dropped from 34.8% to 67.9%, and the accuracy rate increased by 12.5 percentage points. This method performs better in 76.4% of scenarios, reducing computing resource consumption by 21.3%. This method can improve data availability and processing efficiency while ensuring data privacy.