Mobile apps often access personal information to meet business needs, raising concerns about privacy breaches. Compliance detection methods are proposed to check for inconsistencies between program code and privacy policies. However, existing methods face challenges with the low efficiency of static data flow analysis tools and often neglect physical data transmission destinations.

To address these issues, we propose an automated compliance detection method called GNChecker. It uses an efficient static data flow analysis technique with a segmentation strategy, significantly reducing the search scope and improving efficiency. Additionally, a fine-grained consistency detection framework is proposed by integrating static data flow and dynamic traffic flow results into a unified tuple form, i.e., (information type, transmission address). Evaluation results on 50 popular apps show that GNChecker outperforms state-of-the-art data flow analysis tools. Among 1,134 real-world apps, GNChecker identified 1,410 true non-compliant transmission behaviors in 379 apps, significantly surpassing existing compliance detection tools.