Study Dataset: Advertisements and Recommendations of Apple's and Google's EU App Stores

This is the study dataset from the paper "Ad Personalization and Transparency in Mobile Ecosystems: A Comparative Analysis of Google’s and Apple’s EU App Stores" by David Breuer, Lucas Becker, and Matthias Hollick in Proceedings on Privacy Enhancing Technologies 2026 (Issue 1). Paper Abstract: Smartphones have become the primary interface to the Internet for many users, making app stores an essential part of the mobile ecosystem. Apple's App Store and Google's Play Store form a duopoly of the two largest app stores, both of which offer targeted advertisements in their store ecosystems. Consequently, their need for user data to improve the targeting of ads conflicts with users' desire for privacy. Users have to trust the statements given in privacy policies that are often scattered over multiple places and they have no way of overseeing how their data is used for ad targeting. The European Union passed several regulations, most notably the DSA and DMA, addressing this transparency issue. The implementation of these laws, however, must be audited to ensure their effectiveness. Unfortunately, the transparency measures implemented in the context of advertising and the ad-targeting mechanisms in app stores have received little attention so far. In this work, we analyze the first-party ad tracking ecosystem on Apple's and Google's app stores. We measure the effects of different account parameters and interest patterns on the ads these accounts receive. Furthermore, we study the transparency measures implemented by the platforms. While we only detect rare occurrences of targeted advertising, we find Google's recommendations to be highly personalized. We notice multiple issues with the realization of transparency measures that affect their effectiveness and, in our opinion, contradict corresponding EU laws. Analysis Code: Our complete source code can be found on https://github.com/seemoo-lab/appstore-ad-tools. Acknowledgements: This work has been co-funded by the Federal Ministry of Education and Research of Germany in the project Open6GHub (grant number: 16KISK014) and the German Research Foundation (DFG) in the project CRUST (grant number: 503199853).