Personal Information Protection in Government Data Openness Using Decision Tree Model

Abstract

To address the issues of personal privacy and information security in the current digital age, this study first collects and analyzes relevant government data openness policies and personal information protection laws and regulations to understand the current policy and legal environment. Secondly, a complexity pruning decision tree model is constructed, which can identify and evaluate potential personal information protection risks in government data openness. Using the Singapore government open dataset, this decision tree model is applied for empirical analysis, and its accuracy and effectiveness are evaluated. The research results demonstrate that the complexity pruning decision tree model performs well in terms of accuracy, recall rate, F1 score, and the area under the ROC Curve (AUC). The model achieves an accuracy of 0.85 on the training and 0.8 on the test sets, indicating its high performance in personal information protection in government data openness.