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

  • Authors:
  • William Enck;Peter Gilbert;Byung-Gon Chun;Landon P. Cox;Jaeyeon Jung;Patrick McDaniel;Anmol N. Sheth

  • Affiliations:
  • The Pennsylvania State University;Duke University;Intel Labs;Duke University;Intel Labs;The Pennsylvania State University;Intel Labs

  • Venue:
  • OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
  • Year:
  • 2010

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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.