AppProfiler:一种向终端用户暴露android应用中与隐私相关行为的灵活方法

S. Rosen, Zhiyun Qian, Z. Morley Mao
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引用次数: 129

摘要

尽管Android的权限系统旨在让用户对自己的隐私做出明智的决定,但它在传达有意义的、有用的信息方面往往是无效的,比如用户的隐私可能会因使用应用程序而受到怎样的影响。我们提出了另一种方法,为用户提供对所安装的应用程序做出明智决策所需的知识。首先,我们创建API调用和细粒度隐私相关行为之间映射的知识库。然后,我们使用这个知识库,通过静态分析,生成应用程序行为的高级行为概要。到目前为止,我们已经分析了近80,000个应用程序,并通过Android应用程序和在线提供了结果行为配置文件。到目前为止,已有近1500名用户使用了这个应用程序。基于2782条特定于应用程序的反馈,我们分析了用户对应用程序如何影响其隐私的看法,并证明这些配置文件对他们对这些应用程序的理解产生了实质性影响。我们还展示了这些概要文件在理解应用程序行为的大规模趋势以及对用户隐私的影响方面的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AppProfiler: a flexible method of exposing privacy-related behavior in android applications to end users
Although Android's permission system is intended to allow users to make informed decisions about their privacy, it is often ineffective at conveying meaningful, useful information on how a user's privacy might be impacted by using an application. We present an alternate approach to providing users the knowledge needed to make informed decisions about the applications they install. First, we create a knowledge base of mappings between API calls and fine-grained privacy-related behaviors. We then use this knowledge base to produce, through static analysis, high-level behavior profiles of application behavior. We have analyzed almost 80,000 applications to date and have made the resulting behavior profiles available both through an Android application and online. Nearly 1500 users have used this application to date. Based on 2782 pieces of application-specific feedback, we analyze users' opinions about how applications affect their privacy and demonstrate that these profiles have had a substantial impact on their understanding of those applications. We also show the benefit of these profiles in understanding large-scale trends in how applications behave and the implications for user privacy.
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