Abstract
The increasing concern in user privacy misuse has accelerated research into checking consistencies between smartphone apps' declared privacy policies and their actual behaviors. Recent advances in Large Language Models (LLMs) have introduced promising techniques for semantic comparison, but these methods often suffer from low accuracies and expensive computational costs. To address this problem, this paper proposes a novel hybrid approach that integrates 1) knowledge graph-based deterministic checking to ensure higher accuracy, and 2) LLMs exclusively used for preliminary semantic analysis to save computational costs. Preliminary evaluation indicates this hybrid approach not only achieves 37.63% increase in precision and 23.13% increase F1-score but also consumes 93.5% less tokens and 87.3% shorter time. ©2025 IEEE
| Original language | English |
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| Title of host publication | 2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C) |
| Publisher | IEEE Computer Society Conference Publishing Services (CPS) |
| Pages | 771-772 |
| ISBN (Electronic) | 978-1-6654-7773-4 |
| ISBN (Print) | 978-1-6654-7774-1 |
| DOIs | |
| Publication status | Published - Oct 2025 |
| Event | The 25th International Conference on Software Quality, Reliability, and Security (QRS 2025) - Hangzhou, China Duration: 16 Jul 2025 → 20 Jul 2025 https://qrs25.techconf.org |
Conference
| Conference | The 25th International Conference on Software Quality, Reliability, and Security (QRS 2025) |
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| Abbreviated title | QRS 2025 |
| Place | China |
| City | Hangzhou |
| Period | 16/07/25 → 20/07/25 |
| Internet address |
Bibliographical note
Information for this record is supplemented by the author(s) concerned.Research Keywords
- Privacy Alignment
- Privacy Testing
- Large Language Models
- Knowledge Graph
- Static Analysis