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Over the past few years, the two dominant app platforms made major improvements to their policies surrounding child-directed apps. While prior work repeatedly demonstrated that privacy issues were prevalent in child-directed apps, it is unclear whether platform policies can lead child-directed apps to comply with privacy requirements, when laws alone have not. To understand the effect of recent changes in platform policies (e.g., whether they result in greater levels of compliance with applicable privacy laws), we conducted a large-scale measurement study of the privacy behaviors of 7,377 child-directed Android apps, as well as a follow-up survey with some of their developers. We observed a drastic decrease in the number of apps that transmitted personal data without verifiable parental consent and an increase in the number of apps that encrypted their transmissions using TLS. However, improper use of third-party SDKs still led to privacy issues (e.g., inaccurate disclosures in apps’ privacy labels). Our analysis of apps’ privacy practices over a period of a few months in 2023 and a comparison of our results with those observed a few years ago demonstrate gradual improvements in apps’ privacy practices over time. We discuss how app platforms can further improve their policies and emphasize the role of enforcement in making such policies effective. 

Like most modern software, secure messaging apps rely on third-party components to implement important app functionality. Although this practice reduces engineering costs, it also introduces the risk of inadvertent privacy breaches due to misconfiguration errors or incomplete documentation. Our research investigated secure messaging apps' usage of Google's Firebase Cloud Messaging (FCM) service to send push notifications to Android devices. We analyzed 21 popular secure messaging apps from the Google Play Store to determine what personal information these apps leak in the payload of push notifications sent via FCM. Of these apps, 11 leaked metadata, including user identifiers (10 apps), sender or recipient names (7 apps), and phone numbers (2 apps), while 4 apps leaked the actual message content. Furthermore, none of the data we observed being leaked to FCM was specifically disclosed in those apps' privacy disclosures. We also found several apps employing strategies to mitigate this privacy leakage to FCM, with varying levels of success. Of the strategies we identified, none appeared to be common, shared, or well-supported. We argue that this is fundamentally an economics problem: incentives need to be correctly aligned to motivate platforms and SDK providers to make their systems secure and private by default. 

Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy. In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus. Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work. 

It is commonly assumed that “free” mobile apps come 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 comparing the privacy practices of free apps and their paid premium versions, while also gauging consumer expectations surrounding free and paid apps. We use both static and dynamic analysis to examine 5,877 pairs of free Android apps and their paid counterparts for differences in data collection practices and privacy policies between pairs. To understand user expectations for paid apps, we conducted a 998-participant online survey and found that consumers expect paid apps to have better security and privacy behaviors. However, there is no clear evidence that paying for an app will actually guarantee protection from extensive data collection in practice. Given that the free version had at least one thirdparty library or dangerous permission, respectively, we discovered that 45% of the paid versions reused all of the same third-party libraries as their free versions, and 74% of the paid versions had all of the dangerous permissions held by the free app. Likewise, our dynamic analysis revealed that 32% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Finally, we found that 40% of apps did not have a privacy policy link in the Google Play Store and that only 3.7% of the pairs that did reflected differences between the free and paid versions. 

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.