TaintDroid

W. Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, P. Mcdaniel, Anmol Sheth
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引用次数: 3385

Abstract

Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32% performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.
如今的智能手机操作系统经常无法向用户提供第三方应用程序如何收集和共享他们的私人数据的可见性。我们用TaintDroid解决了这些缺点,这是一个高效的,系统范围的动态污染跟踪和分析系统,能够同时跟踪多个敏感数据来源。TaintDroid通过利用Android的虚拟化执行环境实现实时分析。在cpu绑定的微基准测试中,TaintDroid只带来32%的性能开销,对交互式第三方应用程序的开销可以忽略不计。在我们2010年的研究中,使用TaintDroid监控30个流行的第三方Android应用程序的行为,我们发现20个应用程序可能滥用用户的私人信息;在我们2012年的研究中,同样比例的测试应用程序也是如此。使用TaintDroid监控隐私敏感数据流为智能手机用户和安全服务公司提供了有价值的输入,帮助他们识别行为不端的应用程序。
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