Consistency Analysis of Data-Usage Purposes in Mobile Apps
Consistency Analysis of Data-Usage Purposes in Mobile Apps
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DOI：
10.1145/3460120.3484536
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发表时间：
2021-11
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影响因子：
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通讯作者：
D. Bui;Y. Yao;K. Shin;Jong-Min Choi;Junbum Shin
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文献类型：
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作者：
D. Bui;Y. Yao;K. Shin;Jong-Min Choi;Junbum Shin
虽然隐私法律和法规要求应用程序和服务向用户披露其数据收集的目的（即，他们为什么要收集我的数据？），应用程序实际行为中的数据使用并不总是符合其隐私政策中规定的目的。已经提出了自动化技术来分析应用程序的隐私政策及其执行行为，但它们往往忽略了应用程序收集，使用和共享数据的目的。为了减轻这种疏忽，我们提出PurPliance，一个自动化的系统，检测在自然语言隐私政策和Android应用程序的实际执行行为中所述的数据使用目的之间的不一致。PurPliance分析策略语句的谓词-参数结构，并将提取的目的子句分类到数据目的的分类中。实际数据使用的目的是从网络数据流量推断的。我们提出了一个正式的模型来表示和验证提取的隐私声明和数据流中的数据使用目的，以检测隐私策略中的策略矛盾和网络数据流与隐私声明之间的流到策略不一致。我们对端到端矛盾检测的评估结果表明，与最先进的方法相比，PurPliance将检测精度从19%提高到95%，召回率从10%提高到50%。我们对23.1k个Android应用程序的分析还表明，PurPliance可以检测到18.14%的隐私策略中的矛盾，以及69.66%的应用程序中的流到策略不一致，这表明移动的应用程序中数据实践不一致的普遍性。
While privacy laws and regulations require apps and services to disclose the purposes of their data collection to the users (i.e., why do they collect my data?), the data usage in an app's actual behavior does not always comply with the purposes stated in its privacy policy. Automated techniques have been proposed to analyze apps' privacy policies and their execution behavior, but they often overlooked the purposes of the apps' data collection, use and sharing. To mitigate this oversight, we propose PurPliance, an automated system that detects the inconsistencies between the data-usage purposes stated in a natural language privacy policy and those of the actual execution behavior of an Android app. PurPliance analyzes the predicate-argument structure of policy sentences and classifies the extracted purpose clauses into a taxonomy of data purposes. Purposes of actual data usage are inferred from network data traffic. We propose a formal model to represent and verify the data usage purposes in the extracted privacy statements and data flows to detect policy contradictions in a privacy policy and flow-to-policy inconsistencies between network data flows and privacy statements. Our evaluation results of end-to-end contradiction detection have shown PurPliance to improve detection precision from 19% to 95% and recall from 10% to 50% compared to a state-of-the-art method. Our analysis of 23.1k Android apps has also shown PurPliance to detect contradictions in 18.14% of privacy policies and flow-to-policy inconsistencies in 69.66% of apps, indicating the prevalence of inconsistencies of data practices in mobile apps.