Abstract: Privacy policies serve as self-regulatory commitments by online service providers to legitimize the collection and utilization of personal information,aiming to enhance user trust and provide users with greater control over data processing.However,they face practical challenges including excessive length,technical jargon proliferation,and ambiguities in legal compliance.Traditional approaches rely on classification models that detect compliance through annotated policy texts.However,these methods suffer from oversimplified evaluation metrics,high annotation costs,and limited detection accuracy.This paper proposes a large language model(LLM)-based framework for mobile App privacy policy compliance detection,structured around three pillars:(1)establishing a multi-tier compliance evaluation system,(2)designing a hierarchical reasoning framework enhanced by Dynamic Optimal Trajectory Search(DOTS),and(3)implementing automated compliance verification.Firstly,this paper constructs a compliance evaluation system comprising 6 first-level,14 second-level,and 41 third-level indicators,grounded in nine legal frameworks including China's Civil Code and Personal Information Protection Law.Secondly,it develops the Dynamic Tri-Stage Hierarchical Compliance Evaluator(DOTS-THCE),a three-phase reasoning framework that enables few-shot prompting to guide LLMs in conducting multi-level dynamic assessments of privacy policies.Finally,it implements automated detection on the PPC-Bench dataset containing 4 821 privacy policies across 10 application categories collected from Tencent's “MyApp” store.Experimental results demonstrate that the Qwen2.5-7B-Instruct model augmented with DOTS-THCE outperforms baseline models(Deepseek-LLM-7B-Chat,Llama3.1-8B-Chinese-Chat,and GLM-4-9B-Chat) by a significant margin.The Qwen2.5-7B-Instruct@DOTS-THCE configuration achieves a macro-F1 score of 89.30%,surpassing traditional models including SVM,CNN,RNN,BERT,and Qwen2.5-7B-Instruct@RAG in terms of detection efficacy.This study not only pioneers LLM applications in privacy policy compliance detection,but also provides methodological insights for addressing data annotation scarcity in judicial AI systems.

Key words: Privacy policy, Compliance detection, Dynamic optimal trajectory search, Dynamic tri-stage hierarchical compliance evaluator, Large language model