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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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Journal ArticleDOI

Dexteroid: Detecting malicious behaviors in Android apps using reverse-engineered life cycle models

TL;DR: A static analysis framework called Dexteroid is presented, which uses reverse-engineered life cycle models to accurately capture the behaviors of Android components and shows that the proposed framework is effective and efficient in terms of precision, recall, and execution time.
Dissertation

Matching user accounts across online social networks : methods and applications

Oana Goga
TL;DR: This thesis develops methods to identify the social networks accounts of a given user and demonstrates that, even if users are privacy conscious and maintain distinct profiles on different social networks, they can still potentially match their accounts.
Proceedings ArticleDOI

Implementing and Optimizing an Encryption Filesystem on Android

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Proceedings ArticleDOI

Side-channel attacks on mobile and wearable systems

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Proceedings ArticleDOI

A Study of Grayware on Google Play

TL;DR: It is hypothesized enhancing analysis with text analytics can effectively reduce human effort when triaging grayware, and it is shown how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
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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