CPS-LSTM: Privacy-Sensitive Entity Adaptive Recognition Model for Power Systems

With the widespread application of Android devices in the energy sector, an increasing number of applications rely on SDKs to access privacy-sensitive data, such as device identifiers, location information, energy consumption, and user behavior. However, these data are often stored in different formats and naming conventions, which poses challenges for consistent extraction and identification. Traditional taint analysis methods are inefficient in identifying these entities, hindering the realization of accurate identification. To address this issue, we first propose a high-quality data construction method based on privacy protocols, which includes sentence segmentation, compression encoding, and entity annotation. We then introduce CPS-LSTM (Character-level Privacy-sensitive Entity Adaptive Recognition Model), which enhances the recognition capability of privacy-sensitive entities in mixed Chinese and English text through character-level embedding and word vector fusion. The model features a streamlined architecture, accelerating convergence and enabling parallel sentence processing. Our experimental results demonstrate that CPS-LSTM significantly outperforms the baseline methods in terms of accuracy and recall. The accuracy of CPS-LSTM is 0.09 higher than Lattice LSTM, 0.14 higher than WC-LSTM, and 0.05 higher than FLAT. In terms of recall, CPS-LSTM is 0.07 higher than Lattice LSTM, 0.12 higher than WC-LSTM, and 0.02 higher than FLAT.