One of the core privacy features of smartphone operating systems is a permission framework that requires explicit user consent before granting apps access to private data. Such systems are deeply integrated into Google's Android and Apple's iOS, which together account for the majority of the smartphone operating system market. While permission systems can be seen as milestones in user empowerment and privacy protection, they offer users only a binary choice: whether an app can access a specific resource or not. As soon as an app is allowed to read a resource, the operating system loses control over its further use. Most apps have Internet access and can send permission-protected data, like a user's location, over the Internet, which can harm user privacy. To solve this problem, we present an addition to current permission systems that splits apps into multiple sandboxed processes to enforce fine-grained privacy and data-flow controls on smartphones. By default, our design forces apps to process permission-protected data locally on the device, thereby eliminating the need for apps to request runtime permissions for local-only use cases. We implement a proof-of-concept based on the Android Open Source Project code base. We showcase our framework's practicability by adapting multiple app use cases to our system, benchmarking its computational overhead, and discussing the implications for platform operators, developers, and users.

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 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.

SUMMARY: Automated analysis of imaged phenotypes enables fast and reproducible quantification of biologically relevant features. Despite recent developments, recordings of complex networked structures, such as leaf venation patterns, cytoskeletal structures or traffic networks, remain challenging to analyze. Here we illustrate the applicability of img2net to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties and statistically comparing networks of different types or under different conditions. The software can be readily used for analyzing image data of arbitrary 2D and 3D network-like structures. AVAILABILITY AND IMPLEMENTATION: img2net is open-source software under the GPL and can be downloaded from http://mathbiol.mpimp-golm.mpg.de/img2net/, where supplementary information and datasets for testing are provided. CONTACT: [email protected].