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Open AccessProceedings ArticleDOI

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.
References
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Proceedings ArticleDOI

A General Dynamic Information Flow Tracking Framework for Security Applications

TL;DR: This paper presents a general dynamic information flow tracking framework (called GIFT) for C programs that allows an application developer to associate application-specific tags with input data, instruments the application to propagate these tags to all the other data that are control/data-dependent on them, and invokes application- specific processing on output data according to their tag values.
Proceedings Article

Tightlip: keeping applications from spilling the beans

TL;DR: TightLip is a privacy management system that helps users define what data is sensitive and who is trusted to see it rather than forcing them to understand or predict how the interactions of their software packages can leak data.
Journal ArticleDOI

Labels and event processes in the Asbestos operating system

TL;DR: A new event process abstraction defines lightweight, isolated contexts within a single process, allowing one process to act on behalf of multiple users while preventing it from leaking any single user's data to others.
Proceedings ArticleDOI

CleanOS: limiting mobile data exposure with idle eviction

TL;DR: This paper presents CleanOS, a new Android-based operating system that manages sensitive data rigorously and maintains a clean environment at all times and instrumented Android's Dalvik interpreter to securely evict that data after a specified period of non-use.
Book ChapterDOI

Implicit Flows: Can't Live with `Em, Can't Live without `Em

TL;DR: Experimentally investigates the explicit and implicit flows identified by the standard algorithm for establishing noninterference, and concludes with some ideas to improve the false alarm rate, toward making stronger security analysis more practical.
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