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Article

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

Authors:

Byung-Gon Chun,

Patrick McDaniel,

Anmol N. ShethAuthors Info & Claims

OSDI'10: Proceedings of the 9th USENIX conference on Operating systems design and implementation

Pages 393 - 407

Published: 04 October 2010 Publication History

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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Published In

cover image ACM Other conferences

OSDI'10: Proceedings of the 9th USENIX conference on Operating systems design and implementation

October 2010

386 pages

Program Chairs:
Remzi Arpaci-Dusseau
University of Wisconsin, Madison
,
Brad Chen
Google, Inc.

Sponsors

NSF: National Science Foundation
Google Inc.
Infosys
Microsoft Research: Microsoft Research
USENIX Assoc: USENIX Assoc

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

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USENIX Association

United States

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Published: 04 October 2010

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Cited By

Zhang YLiu TWang YQi YJi KTang JWang XLi XZuo Z(2024)HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware TracingProceedings of the ACM on Programming Languages10.1145/36897688:OOPSLA2(1615-1640)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689768
Moiz AAlalfi M(2022)A survey of security vulnerabilities in Android automotive appsProceedings of the 3rd International Workshop on Engineering and Cybersecurity of Critical Systems10.1145/3524489.3527300(17-24)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1145/3524489.3527300
Kollnig KBinns RDewitte PVan Kleek MWang GOmeiza DWebb HShadbolt NChiasson S(2021)A fait accompli? an empirical study into the absence of consent to third-party tracking in android appsProceedings of the Seventeenth USENIX Conference on Usable Privacy and Security10.5555/3563572.3563582(181-195)Online publication date: 9-Aug-2021
https://dl.acm.org/doi/10.5555/3563572.3563582
Hough KBell J(2021)A Practical Approach for Dynamic Taint Tracking with Control-flow RelationshipsACM Transactions on Software Engineering and Methodology10.1145/348546431:2(1-43)Online publication date: 24-Dec-2021
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Rahaman SNeamtiu IYin XSpinellis DGousios GChechik MDi Penta M(2021)Algebraic-datatype taint tracking, with applications to understanding Android identifier leaksProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468550(70-82)Online publication date: 20-Aug-2021
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Krupp BHadden JMatthews M(2021)An Analysis of Web Tracking Domains in Mobile ApplicationsProceedings of the 13th ACM Web Science Conference 202110.1145/3447535.3462507(291-298)Online publication date: 21-Jun-2021
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