基于多模态融合的移动应用细粒度用户意图理解
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工信部专项(TC220H079)


Fine-grained User Intention Understanding for Mobile Applications Based on Multi-modality Fusion
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    摘要:

    随着移动应用功能日益复杂, 现有基于用户意图的隐私泄露检测方法面临更大挑战. 一方面, 传统隐私泄露检测基于应用级别的用户意图, 只关注应用的隐私收集行为是否与应用的核心功能需求相符合, 不适用于现如今具有广泛功能和多元用户意图的移动应用安全检测, 亟需粒度更细的用户意图分类; 另一方面, 现行研究大多集中于评估图标等界面小部件触发的隐私收集行为是否与用户意图一致, 然而, 图标不当设计和滥用现象十分普遍, 这限制了仅依赖小部件用户意图进行隐私风险评估的有效性, 因此当前仍需要对整体用户界面的意图进行理解. 针对以上问题, 本文首先从中文隐私政策中提取总结出常见的、适用于隐私合规判断的细粒度用户意图列表; 之后结合移动应用界面设计特点, 设计并实现了多模态特征融合的多分类模型对整个移动界面反应的用户意图进行识别. 评估结果表明, 本文隐私政策意图提取工具精确率与召回率均达到83%, 用户意图识别工具精确率与召回率分别达到了80%与83%, 具有较好的检测效果与实际可用性.

    Abstract:

    With the increasing complexity of mobile applications, existing privacy leak detection methods based on user intent face greater challenges. On the one hand, traditional privacy leak detection, which is based on APP-level user intent, only focuses on whether the privacy collection behavior of the application aligns with its core functional requirements. This approach is not suitable for today’s mobile APP security detection, which has broad functionalities and diverse user intents, necessitating a more fine-grained user intent classification. On the other hand, current research mainly focuses on evaluating whether the privacy collection behaviors triggered by interface widgets, such as icons, are consistent with user intent. However, the improper design and misuse of icons are very common, which limits the effectiveness of privacy risk assessments that rely solely on widget-based user intents. Therefore, a comprehensive understanding of user intent at the overall interface level is still needed. In response to the above issues, this study first extracts and summarizes a fine-grained user intent list suitable for privacy compliance detection based on Chinese privacy policies. Then, based on the characteristics of mobile application interface design, a multi-classification model with multi-modal feature fusion is designed and implemented to identify the user intent reflected by the entire mobile interface. Evaluation results show that the intent extraction tool in this study has achieved 83% in both precision and recall, and the user intent classification model reaches 80% and 83% in precision and recall, respectively, demonstrating good detection effectiveness and practical usability.

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张逸涵,洪赓,杨哲慜.基于多模态融合的移动应用细粒度用户意图理解.计算机系统应用,,():1-15

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  • 收稿日期:2024-04-07
  • 最后修改日期:2024-05-06
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  • 在线发布日期: 2024-09-24
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