Efficient, context-aware privacy leakage confinement for android applications without firmware modding

Abstract

As Android has become the most prevalent operating system in mobile devices, privacy concerns in the Android platform are increasing. A mechanism for efficient runtime enforcement of informationflow security policies in Android apps is desirable to confine privacy leakage. The prior works towards this problem require firmware modification (i.e., modding) and incur considerable runtime overhead. Besides, no effective mechanism is in place to distinguish malicious privacy leakage from those of legitimate uses. In this paper, we take a bytecode rewriting approach. Given an unknown Android app, we selectively insert instrumentation code into the app to keep track of private information and detect leakage at runtime. To distinguish legitimate and malicious leaks, we model the user's decisions with a context-aware policy enforcement mechanism. We have implemented a prototype called Capper and evaluated its efficacy on confining privacy-breaching apps. Our evaluation on 4723 real-world Android applications demonstrates that Capper can effectively track and mitigate privacy leaks. Moreover, after going through a series of optimizations, the instrumentation code only represents a small portion (4.48% on average) of the entire program. The runtime overhead introduced by Capper is also minimal, merely 1.5% for intensive data propagation.

Towards Understanding the Advertiser's Perspective of Smartphone User Privacy

“…AppIntent [29] tracks the sequence of events leading to private data transmission, which helps to determine whether it is user intended or not. Zhang and Yin [30] develop Capper that can track privacy information and detect leakage at run time by inserting instrumentation code into apps. Our work takes a different viewpoint by systematically analyzing what privacy sensitive information the advertiser can collect and aggregate at run-time from multiple apps and infer the social relationship of a user.…”

DroidRA: taming reflection to support whole-program analysis of Android apps

“…AppSealer [49] instruments Android apps for generating vulnerability-specific patches, which prevent component hijacking attacks at runtime. Other approaches [50,51] apply the same idea, which injects shadow code into Android apps, to perform privacy leaks prevention.…”

Harvesting developer credentials in Android apps

“…For example, TaintDroid [21] uses dynamic taint analysis to detect privacy leaks in Android apps, while PiOS [20] targets the iOS platform. Several systems [16,29,32,40,42,47,48,48,50] have been proposed to mitigate this threat. They either extend the Android framework to provide fine-grained permission control, or repackage apps to avoid changing the framework.…”