FlowIntent: Detecting Privacy Leakage from User Intention to Network Traffic Mapping
This addresses privacy leakage concerns for mobile device users by providing a more adaptive and deployable detection method compared to existing network- and system-level schemes.
The paper tackles the problem of detecting privacy leakage in mobile apps by identifying suspicious location-related HTTP transmissions from the user's perspective, achieving about 91% accuracy in detecting illegitimate location transmissions.
The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of-concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.