ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Ahn, Gail-Joon
Only when the user approves the requested permissions will the app be installed.
However, permissions are an incomplete security mechanism.
In addition to a user's limited understanding of permissions, the mechanism does not account for the possibility that different permissions used together have the ability to be more dangerous than any single permission alone.
Even if users did understand the nature of an app's requested permissions, this mechanism is still not enough to guarantee that a user's information is protected.
Applications can potentially send or receive sensitive information from other applications without the required permissions by using intents.
In other words, applications can potentially collaborate in ways unforeseen by the user, even if the user understands the permissions of each app independently.
In this thesis, we present several graph-based approaches to address these issues.
We determine the permissions of an app and generate scores based on our assigned value of certain resources.
We analyze these scores overall, as well as in the context of the app's category as determined by Google Play.
We show that these scores can be used to identify overzealous apps, as well as apps that do not properly fit within their category.
We analyze potential interactions between different applications using intents, and identify several promiscuous apps with low permission scores, showing that permissions alone are not sufficient to evaluate the security risks of an app.
Our analyses can form the basis of a system to assist users in identifying apps that can potentially compromise user privacy.