Precursor of privacy leakage detection for individual user

The pursuit of dividends from the burgeoning realm of data traffic has emerged as a prevailing trend. Given this current state of affairs, the safeguarding of personal information has become an urgent task. Current methods for personal privacy protection primarily offer alerts when information breaches occur, but at that stage, irreversible breaches have already transpired. Thus, the study of preemptive privacy breach prediction is a task of significant importance within the realm of privacy protection. However, the endeavor to predict privacy breaches in advance remains exceedingly challenging, owing to several factors: (i) The complexity of social networks gives rise to high-dimensional features. (ii) Concerning the comprehensive and precise capture of pathways leading to information leakage. (iii) How to employ time series data effectively to realize early predictions. This study proposes a novel approach for constructing graph neural networks and forecasting privacy breaches, centered on the context of user-generated content within specific time frames, integrates spatial graph structures with temporal series information. The integration can entirely achieve the advance prediction of users' ' privacy status, thereby preventing information leakage. Empirical tests on real-world datasets demonstrate that our approach surpasses traditional time series forecasting methods in privacy breach predictions, achieving a notable average improvement of 2% in F1 score, Recall, and Precision metrics.