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Mobile devices are becoming increasingly popular. One reason for their popularity is the availability of a wide range of third-party applications, which enrich the environment and increase usability. There are however privacy concerns centered around these applications -users do not know what private data is leaked by the applications. Previous works to detect privacy leakages are either not accurate enough or require operating system changes, which may not be possible due to users' lack of skills or locked devices. We present Uranine 1 , a system that instruments Android applications to detect privacy leakages in real-time. Uranine does not require any platform modification nor does it need the application source code. We designed several mechanisms to overcome the challenges of tracking information flow across framework code, handling callback functions, and expressing all information-flow tracking at the bytecode level. Our evaluation of Uranine shows that it is accurate at detecting privacy leaks and has acceptable performance overhead.

The booming popularity of smartphones is partly a result of application markets where users can easily download wide range of third-party applications. However, due to the open nature of markets, especially on Android, there have been several privacy and security concerns with these applications. On Google Play, as with most other markets, users have direct access to natural-language descriptions of those applications, which give an intuitive idea of the functionality including the security-related information of those applications. Google Play also provides the permissions requested by applications to access security and privacy-sensitive APIs on the devices. Users may use such a list to evaluate the risks of using these applications. To best assist the end users, the descriptions should reflect the need for permissions, which we term description-to-permission fidelity. In this paper, we present a system AutoCog to automatically assess description-to-permission fidelity of applications. AutoCog employs state-of-the-art techniques in natural language processing and our own learning-based algorithm to relate description with permissions. In our evaluation, AutoCog outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent. On an evaluation of eleven permissions, we achieve an average precision of 92.6% and an average recall of 92.0%. Our large-scale measurements over 45,811 applications demonstrate the severity of the problem of low description-to-permission fidelity. AutoCog helps bridge the long-lasting usability gap between security techniques and average users.