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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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State-of-the-Art Resources (SOAR) for Software Vulnerability Detection, Test, and Evaluation

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A Secure Architecture for Operating System-Level Virtualization on Mobile Devices

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AnaDroid: Malware Analysis of Android with User-supplied Predicates

TL;DR: A semantic-based program analysis, with a human in the loop, is proposed that provides analysts with a library of the semantic predicates and the ability to dynamically trade speed and precision in order to statically inspect details of every suspicious state of (abstract) execution.

DroidAuditor: Forensic Analysis of Application-Layer Privilege Escalation Attacks on Android

TL;DR: This work has shown that fine-grained access control architectures in smart mobile devices are susceptible to application-layer privilege escalation attacks, and while state-of-the-art static analysis frameworks cannot adequately address native and highly obfuscated code, dynamic analysis is vulnerable to malicious applications using logic bombs to avoid early detection.
References
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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.
Journal ArticleDOI

Language-based information-flow security

TL;DR: A structured view of research on information-flow security is given, particularly focusing on work that uses static program analysis to enforce information- flow policies, and some important open challenges are identified.
Journal ArticleDOI

A lattice model of secure information flow

TL;DR: The model provides a unifying view of all systems that restrict information flow, enables a classification of them according to security objectives, and suggests some new approaches to formulating the requirements of secure information flow among security classes.
Proceedings Article

Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software

TL;DR: TaintCheck as mentioned in this paper performs dynamic taint analysis by performing binary rewriting at run time, which can reliably detect most types of exploits and produces no false positives for any of the many different programs that were tested.
Proceedings ArticleDOI

JFlow: practical mostly-static information flow control

TL;DR: The new language JFlow is described, an extension to the Java language that adds statically-checked information flow annotations and provides several new features that make information flow checking more flexible and convenient than in previous models.
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