PrivSniffer: Graph-based Contextual Privacy Leakage Detection for User-Generated Texts

A vast amount of user-generated content is uploaded on social media everyday, potentially being collected as training data for large language models, and thus poses severe threats to individual privacy. Existing approaches for protecting user-generated content often fail to detect implicit privacy leaks which are not directly given but could be inferred from the contextual text (clues). The precise detection of leaks and clues is difficult but critical in text sanitization. We propose a graph-based model for the user-generated content and build a privacy leakage detector PrivSniffer which integrates the inference capability of language models with the precise graph search, to capture the contextual privacy leakage (CPL). To evaluate the effectiveness of the framework, we create SynthLeak, a dataset of dialogues containing human-labeled implicit leaks, while featuring diversity and naturalness. Our experimental results on SynthLeak and several other benchmarks reveal PrivSniffer's superior capability in detecting implicit leaks and CPLs. Particularly in CPL detection, the state-of-the-art approach achieves only an F1 Score of 0.36, whereas PrivSniffer attains 0.59 on SynthLeak, showing great promise in real-world text sanitization.