A Knowledge Graph-Driven Framework for Privacy Leakage Risk Assessment in Open-Source Information

Open-source information is characterized by openness, transparency, sharing, and free usability. While these attributes promote knowledge dissemination and technological advancement, they also introduce potential security risks, particularly data leakage. This study aims to analyze and alert privacy leakage risks in open-source information by constructing an event logic graph of open-source information elements. First, we analyze privacy leakage cases in specific domains and build a privacy leakage event logic graph using expert knowledge. Next, we employ keyword analysis to extract key terms from open-source information and integrate them with the constructed knowledge graph to investigate leakage risks and mitigation measures across information processes. These efforts provide novel perspectives and methodologies for understanding and controlling security risks in open-source information.