Introducing Privacy in Screen Event Frequency Analysis for Android Apps
Introducing Privacy in Screen Event Frequency Analysis for Android Apps
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DOI：
10.1109/scam.2019.00037
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Hailong Zhang;S. Latif;Raef Bassily;A. Rountev
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文献类型：
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作者：
Hailong Zhang;S. Latif;Raef Bassily;A. Rountev
移动的应用程序通常使用Google和Facebook等公司提供的分析基础设施来收集有关应用程序性能和用户行为的大量细粒度数据。重要的是要理解和执行这种数据收集的好处（对于应用程序开发人员）和相应的隐私损失（对于应用程序用户）之间的适当权衡。我们的工作重点是屏幕事件频率分析，这是移动的应用程序分析中最流行的数据收集形式之一。我们提出了一个隐私保护的版本，这样的分析使用差分隐私（DP），一个流行的原则性的方法来创建隐私保护分析。我们描述了如何DP可以引入屏幕事件频率分析的移动的应用程序，并演示了这种方法的Android应用程序和谷歌分析框架的一个实例。我们的工作是开发部署拟议的DP解决方案所需的自动化应用程序代码分析、代码重写和运行时处理。实验评估表明，高精度和实用的成本，可以实现开发的隐私保护屏幕事件频率分析。
Mobile apps often use analytics infrastructures provided by companies such as Google and Facebook to gather extensive fine-grained data about app performance and user behaviors. It is important to understand and enforce suitable trade-offs between the benefits of such data gathering (for app developers) and the corresponding privacy loss (for app users). Our work focuses on screen event frequency analysis, which is one of the most popular forms of data gathering in mobile app analytics. We propose a privacy-preserving version of such analysis using differential privacy (DP), a popular principled approach for creating privacy-preserving analyses. We describe how DP can be introduced in screen event frequency analysis for mobile apps, and demonstrate an instance of this approach for Android apps and the Google Analytics framework. Our work develops the automated app code analysis, code rewriting, and run-time processing needed to deploy the proposed DP solution. Experimental evaluation demonstrates that high accuracy and practical cost can be achieved by the developed privacy-preserving screen event frequency analysis.