One aspect of privacy that has not been well explored is privacy for children. To address this issue, we design and evaluate a machine learning model that can predict whether a mobile app is designed for children, which is an important step in helping to enforce the Children's Online Privacy Protection Act (COPPA). We evaluated our model on 1,728 apps from Google Play, and achieved 95% accuracy in identifying apps designed for children. We also applied our model on a set of nearly 1 million free apps from Google Play, and identified almost 68,000 apps for kids. We then conducted a privacy analysis of the usage of third-party libraries for each app, which can help us understand some of the app's privacy-related behaviors. We believe this list can serve as a good start point for further fine-grained privacy analysis on mobile apps for children.

Publication: M. Liu, H. Wang, Y. Guo and J. Hong, "Identifying and Analyzing the Privacy of Apps for Kids", International Workshop on Mobile Computing Systems and Applications (HotMobile'16)(PDF)

The content of a QR code rarely changes because the publisher has no way to customize the system variables of the coding part, e.g., the encoding and decoding rules, contextual input, etc. In this project, we present, Printf, as a novel QR code system that provides an interface for the publishers to define the system variables on how they would like their code output to vary under which type of contextual inputs, which may lead to "one code fits many contexts". Printf prescribes a suite of APIs that supports the contextual input (from many phone sensors) beyond the camera input, such that the code output can dynamically vary according to the publisher-defined rules and various contexts where the code is published. Printf builds decoding libraries that can be downloaded to the decoder app on users' phone. Our experimental results reveal Printf has a high decoding accuracy rate, while providing the context-aware flexibility in code content generation and IoT deployment.

Patent (China): K. Bian, M. Liu, Y. Shi, Q. Chen, S. Zhang, Y. Tian, "Design of dynamic QRcode based on environmental information", patent number: CN 104820855 A(Link)

Although many existing studies have focused on analyzing and detecting permission-related issues in Android apps, most of them have been considering these issues from the perspective of app users. This paper takes a different angle to revisit the permission-related issues from the perspective of app developers. We perform an empirical study to investigate how we can help developers make better decisions on permission uses during app development. The study shows that many permission-related issues can be identified and fixed during the application development phase. In order to help developers to identify and fix these issues, we develop PerHelper, an IDE extension for Android Studio, to automatically infer candidate permission sets, which help guide developers to set permissions more effectively and accurately. We also integrate permission-related bug detection into PerHelper and demonstrate its applicability and flexibility through case studies on a set of open-source Android apps.

Poster won the "Best Poster Award" in Peking University Young Scientists Symposium on Informatics(PDF)

Paper submitted to COMPSAC'16