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TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

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TLDR
Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, this work found 68 instances of misappropriation of users' location and device identification information across 20 applications.
Abstract
Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

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Citations
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Journal ArticleDOI

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

TL;DR: TaintDroid as mentioned in this paper is an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data by leveraging Android's virtualized execution environment.
Proceedings ArticleDOI

Dissecting Android Malware: Characterization and Evolution

TL;DR: Systematize or characterize existing Android malware from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software.
Proceedings ArticleDOI

DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

TL;DR: DREBIN is proposed, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone and outperforms several related approaches and detects 94% of the malware with few false alarms.
Proceedings ArticleDOI

FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps

TL;DR: FlowDroid is presented, a novel and highly precise static taint analysis for Android applications that successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.
Proceedings ArticleDOI

Android permissions: user attention, comprehension, and behavior

TL;DR: It is found that current Android permission warnings do not help most users make correct security decisions, however, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension.
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Proceedings ArticleDOI

Efficient fine-grained binary instrumentationwith applications to taint-tracking

TL;DR: In this paper, a static analysis is proposed to recover some of the higher level structure from x86 binaries, which can be applied in the context of taint tracking, which achieves a substantial reduction in performance overheads.
Proceedings ArticleDOI

From Languages to Systems: Understanding Practical Application Development in Security-typed Languages

TL;DR: It is demonstrated that it is possible to implement real-world systems and policy using security-typed languages, and how the strong guarantees of Jif in conjunction with the policy tools can be used to evaluate security.
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