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

The Advantages of Distributed TCAM Firewalls in Automotive Real-Time...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), J. Scot Ransbottom (Virginia Tech)

Read More

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Meenatchi Sundaram Muthu Selva Annamalai (University College London), Igor Bilogrevic (Google), Emiliano De Cristofaro (University of California, Riverside)

Read More

WIP: Body Posture Analysis as an Objective Measurement for...

Cherin Lim, Tianhao Xu, Prashanth Rajivan (University of Washington)

Read More

An Experimental Study on Attacking Homogeneous Averaging Processes via...

Olsan Ozbay (Dept. ECE, University of Maryland), Yuntao Liu (ISR, University of Maryland), Ankur Srivastava (Dept. ECE, ISR, University of Maryland)

Read More