Research on Data Leakage Detection System for Online Nursing Teaching Platform Based on Deep Learning

Abstract

This study focuses on the issue of data leakage in online nursing teaching platforms. Given that the platform has rich and diverse data and concerns the privacy of teachers and students, data leakage can cause many serious problems. Therefore, a data leakage detection system is constructed using deep learning technology. The research first analyzes the relevant technical foundations, covering the architecture of online nursing teaching platforms, core technologies of deep learning, and traditional data leakage detection techniques. Further design a dual layer architecture detection system, including a data acquisition layer and a deep learning detection layer, and plan the functional modules in detail. In terms of algorithms, a comparative analysis was conducted on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variants (LSTM, GRU). LSTM was selected to construct the detection algorithm, and its model structure and training process were optimized; Simultaneously introducing attention mechanism to improve the GRU model and enhance its ability to process sequential data. Finally, an efficient, accurate, and stable data leakage detection system was successfully built, effectively safeguarding the privacy and rights of users on the online nursing teaching platform, and providing strong support for the safe operation of the platform.