Jiangrong Wu (Sun Yat-sen University), Yuhong Nan (Sun Yat-sen University), Luyi Xing (Indiana University Bloomington), Jiatao Cheng (Sun Yat-sen University), Zimin Lin (Alibaba Group), Zibin Zheng (Sun Yat-sen University), Min Yang (Fudan University)

Cross-app content sharing is one of the prominent features widely used in mobile apps. For example, a short video from one app can be shared to another (e.g., a messaging app) and further viewed by other users. In many cases, such Cross-app content sharing activities could have privacy implications for both the sharer and sharee, such as exposing app users' personal interests.

In this paper, we provide the first in-depth study on the privacy implications of Cross-app content sharing (as we call Cracs) activities in the mobile ecosystem. Our research showed that during the sharing process, the adversary can not only track and infer user interests as traditional web trackers but also cause other severe privacy implications to app users. More specifically, due to multiple privacy-intrusive designs and implementations of Cracs, an adversary can easily reveal a user's social relations to an outside party, or unnecessarily expose user identities and her associated personal data (e.g., user accounts in another app). Such privacy implications are indeed a concern for app users, as confirmed by a user study we have performed with 300 participants.

To further evaluate the impact of our identified privacy implications at large, we have designed an automatic pipeline named Shark, combined with static analysis and dynamic analysis to effectively identify whether a given app introduces unnecessary data exposure in Cracs. We analyzed 300 top downloaded apps collected from app stores in both the US and China. The analysis results showed that over 55% of the apps from China and 10% from the US are indeed problematic.

View More Papers

Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum...

Nicola Ruaro (University of California, Santa Barbara), Fabio Gritti (University of California, Santa Barbara), Robert McLaughlin (University of California, Santa Barbara), Ilya Grishchenko (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara)

Read More

Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

Read More

Resilient Routing for Low Earth Orbit Mega-Constellation Networks

Alexander Kedrowitsch (Virginia Tech), Jonathan Black (Virginia Tech) Daphne Yao (Virginia Tech)

Read More