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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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Actions Speak Louder than Words: Entity-Sensitive Privacy Policy and Data Flow Analysis with PoliCheck
Identifying privacy-sensitive data leaks by mobile applications has been a topic of great research interest for the past decade. Technically, such data flows are not “leaks” if they are disclosed in a privacy policy. To address this limitation in automated analysis, recent work has combined program analysis of applications with analysis of privacy policies to determine the flow-to-policy consistency, and hence violations thereof. However, this prior work has a fundamental weakness: it does not differentiate the entity (e.g., first-party vs. third-party) receiving the privacy-sensitive data. In this paper, we propose POLICHECK, which formalizes and implements an entity-sensitive flow-to-policy consistency model. We use POLICHECK to study 13,796 applications and their privacy policies and find that up to 42.4% of applications either incorrectly disclose or omit disclosing their privacy-sensitive data flows. Our results also demonstrate the significance of considering entities: without considering entity, prior approaches would falsely classify up to 38.4% of applications as having privacy-sensitive data flows consistent with their privacy policies. These false classifications include data flows to third-parties that are omitted (e.g., the policy states only the first-party collects the data type), incorrect (e.g., the policy states the third-party does not collect the data type), and ambiguous (e.g., the policy has conflicting statements about the data type collection). By defining a novel automated, entity-sensitive flow-to-policy consistency analysis, POLICHECK provides the highest-precision method to date to determine if applications properly disclose their privacy-sensitive behaviors.
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- Award ID(s):
- 1817248
- PAR ID:
- 10187038
- Date Published:
- Journal Name:
- Proceedings of the 29th USENIX Security Symposium (USENIX Security '20)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
