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Practical Dynamic Information-Flow Tracking on Mobile Devices

dc.contributor.advisor Lebeck, Alvin R
dc.contributor.advisor Cox, Landon P Pistol, Ion Valentin 2014-08-27T15:21:26Z 2014-08-27T15:21:26Z 2014
dc.description.abstract <p>Today's consumer mobile platforms such as Android and iOS manage large ecosystems of untrusted third-party applications. It is common for an application to request one or more types of sensitive data. Unfortunately, users have no insight into how their data is used. Given the sensitivity of the data accessible by these applications, it is paramount that mobile operating systems prevent apps from leaking it.</p><p>This dissertation shows that it is possible to improve the soundness of dynamic information-flow tracking on a mobile device without sacrificing precision, performance, or transparency. We extend the state of the art in dynamic information-flow tracking on Android and address two major limitations: quantifying implicit flow leaks in Dalvik bytecode and tracking explicit flows in native code. Our goal is to deliver seamless end-to-end taint tracking across Dalvik bytecode and native code.</p><p>We propose SpanDex, a system that quantifies implicit flow leaks in Dalvik bytecode for apps handling password data. SpanDex computes a bound of revealed tainted data by recording the control-flow dependencies and for each password character, keeps track of the possible set of values that have been inferred. We also propose TaintTrap, a taint tracking system for native code in third party apps. We explore native taint tracking performance bottlenecks and hardware acceleration techniques to improve instrumentation performance.</p>
dc.subject Computer science
dc.subject Immunology
dc.subject Computer engineering
dc.subject Android
dc.subject Emulation
dc.subject explicit flows
dc.subject implicit flows
dc.subject TaintDroid
dc.subject TaintTrap
dc.title Practical Dynamic Information-Flow Tracking on Mobile Devices
dc.type Dissertation
dc.department Computer Science

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