It is commonly assumed that the availability of “free” mobile apps comes at the cost of consumer privacy, and that paying for apps could offer consumers protection from behavioral advertising and long-term tracking. This work empirically evaluates the validity of this assumption by investigating the degree to which “free” apps and their paid premium versions differ in their bundled code, their declared permissions, and their data collection behaviors and privacy practices.
We compare pairs of free and paid apps using a combination of static and dynamic analysis. We also examine the differences in the privacy policies within pairs. We rely on static analysis to determine the requested permissions and third-party SDKs in each app; we use dynamic analysis to detect sensitive data collected by remote services at the network traffic level; and we compare text versions of privacy policies to identify differences in the disclosure of data collection behaviors. In total, we analyzed 1,505 pairs of free Android apps and their paid counterparts, with free apps randomly drawn from the Google Play Store’s category-level top charts.
Our results show that over our corpus of free and paid pairs, there is no clear evidence that paying for an app will guarantee protection from extensive data collection. Specifically, 48% of the paid versions reused all of the same third-party libraries as their free versions, while 56% of the paid versions inherited all of the free versions’ Android permissions to access sensitive device resources (when considering free apps that include at least one third-party library and request at least one Android permission). Additionally, our dynamic analysis reveals that 38% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Our exploration of privacy policies reveals that only 45% of the pairs provide a privacy policy of some sort, and less than 1% of the pairs overall have policies that differ between free and paid versions.
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PolyScope: Multi-Policy Access Control Analysis to Compute Authorized Attack Operations in Android Systems
Android’s filesystem access control provides a foundation for system integrity. It combines mandatory (e.g., SEAndroid) and discretionary (e.g., Unix permissions) access control, protecting both the Android platform from Android/OEM ser- vices and Android/OEM services from third-party applications. However, OEMs often introduce vulnerabilities when they add market-differentiating features and fail to correctly reconfigure this complex combination of policies. In this paper, we propose the PolyScope tool to triage Android systems for vulnerabilities using their filesystem access control policies by: (1) identifying the resources that subjects are authorized to use that may be modified by their adversaries, both with and without policy manipulations, and (2) determining the attack operations on those resources that are actually available to adversaries to reveal the specific cases that need vulnerability testing. A key insight is that adversaries may exploit discretionary elements in Android access control to expand the permissions available to themselves and/or vic- tims to launch attack operations, which we call permission expansion. We apply PolyScope to five Google and five OEM Android releases and find that permission expansion increases the privilege available to launch attacks, sometimes by more than 10x, but a significant fraction (about 15-20%) cannot be converted into attack operations due to other system configurations. Based on this analysis, we describe two previously unknown vulnerabilities and show how PolyScope helps OEMs triage the complex combination of access control policies down to attack operations worthy of testing.
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- Award ID(s):
- 1816282
- NSF-PAR ID:
- 10341902
- Editor(s):
- Michael Bailey and Rachel Greenblatt
- Date Published:
- Journal Name:
- 30th USENIX Security Symposium
- Page Range / eLocation ID:
- 2579--2596
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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