PAcT: Detecting and Classifying Privacy Behavior of Android Applications
PAcT: Detecting and Classifying Privacy Behavior of Android Applications
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DOI：
10.1145/3507657.3528543
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发表时间：
2022-05
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影响因子：
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通讯作者：
Vijayanta Jain;Sanonda Datta Gupta;S. Ghanavati;Sai Teja Peddinti;Collin McMillan
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文献类型：
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作者：
Vijayanta Jain;Sanonda Datta Gupta;S. Ghanavati;Sai Teja Peddinti;Collin McMillan
解释和描述移动应用的隐私行为，以确保创建一致和准确的隐私通知，对开发人员来说是一项具有挑战性的任务。创建隐私通知的传统方法基于预定义的模板或调查问卷，并且不依赖于代码中的任何可跟踪行为，这可能会导致不一致和不准确的通知。本文提出了一种自动检测Android应用程序代码中隐私行为的方法。我们开发了隐私行动分类(PACT)，其中包括实践(即应用程序如何使用个人信息)和目的(即为什么)的标签。我们基于标签标注了约5,200个代码段，并创建了一个包含约14,000个标签的多标签多类数据集。我们开发和训练深度学习模型来对代码段进行分类。我们在所有标签类型中获得了最高的F-1分数，在实践和目的方面分别为79.62%和79.02%。
Interpreting and describing mobile applications' privacy behaviors to ensure creating consistent and accurate privacy notices is a challenging task for developers. Traditional approaches to creating privacy notices are based on predefined templates or questionnaires and do not rely on any traceable behaviors in code which may result in inconsistent and inaccurate notices. In this paper, we present an automated approach to detect privacy behaviors in code of Android applications. We develop Privacy Action Taxonomy (PAcT), which includes labels for Practice (i.e. how applications use personal information) and Purpose (i.e. why). We annotate ~5,200 code segments based on the labels and create a multi-label multi-class dataset with ~14,000 labels. We develop and train deep learning models to classify code segments. We achieve the highest F-1 scores across all label types of 79.62% and 79.02% for Practice and Purpose.