PhD Defense: Interaction-Based Privacy Policies for Mobile Apps
Mobile operating systems pervade our modern lives. Security and privacy is of particular concern on these systems, as they have access to a wide range of sensitive resources. Apps access these sensitive resources to help users perform tasks. However, apps may use these sensitive resources in a way that the user does not expect. For example, an app may look up reviews of restaurants nearby, but also leak the user’s location to an ad service every hour.
I claim that interaction serves as a valuable component of security decisions, because the user’s interaction with the app’s user interface (UI) deeply informs their mental model of what is acceptable. I introduce the notion of interaction-based security, wherein security decisions are driven by this interaction.
To help understand and enforce interaction-based security, I present four pieces of work. The first is Redexer, which allows performing binary instrumentation of off-the-shelf Android binaries. Binary transformation is a useful tool for enforcing and studying security properties. I demonstrate one example of how Redexer can be used to study location privacy in apps.
Android permissions constrain how data enters apps, but does not constrain how the information is used or where it goes. Information-flow allows us to formally define what it means for data to leak from applications, but it is unclear how to use information-flow policies for Android apps, because apps frequently declassify infor- mation. My insight is that declassification should be driven by the user’s interaction with the app’s UI. I define interaction-based declassification policies, and show how they can be used to define policies for several example apps. I then implement a symbolic executor which checks Android apps to ensure they respect these policies.
Next, I test the hypothesis that the app’s UI influences security decisions. I outline an app study that measures when apps use sensitive resources with respect to their UI. I then conduct a user study to measure how an app’s UI influences their expectation that a sensitive resource will be accessed. I find that interactivity plays a large role in determining user expectation of sensitive resource use. I also find that users may not always understand background uses of these sensitive resources and using them expectation requires special care in some circumstances.
Last, I present a tool which can help a security auditor quickly understand how apps use resources. My tool uses a novel combination of app logging, symbolic execution, and abstract interpretation to infer a formula that holds on each per- mission use. I evaluate my tool on several moderately-sized apps and show that it infers the same formulas we laboriously found by hand.
Chair: Dr. Jeffrey Foster
Dean’s rep: Dr. Jennifer Golbeck
Members: Dr. Michelle Mazurek
Dr. Dana Dachman-Soled
Dr. David Van Horn