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

OCPPStorm: A Comprehensive Fuzzing Tool for OCPP Implementations (Long)

Gaetano Coppoletta (University of Illinois Chicago), Rigel Gjomemo (Discovery Partners Institute, University of Illinois), Amanjot Kaur, Nima Valizadeh (Cardiff University), Venkat Venkatakrishnan (Discovery Partners Institute, University of Illinois), Omer Rana (Cardiff University)

Read More

Information Based Heavy Hitters for Real-Time DNS Data Exfiltration...

Yarin Ozery (Ben-Gurion University of the Negev, Akamai Technologies inc.), Asaf Nadler (Ben-Gurion University of the Negev), Asaf Shabtai (Ben-Gurion University of the Negev)

Read More

SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

Read More

Don't Interrupt Me – A Large-Scale Study of On-Device...

Marian Harbach (Google), Igor Bilogrevic (Google), Enrico Bacis (Google), Serena Chen (Google), Ravjit Uppal (Google), Andy Paicu (Google), Elias Klim (Google), Meggyn Watkins (Google), Balazs Engedy (Google)

Read More