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

TLDR

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. The authors propose TaintDroid to provide efficient, system‑wide dynamic taint tracking and analysis that gives users real‑time insight into private data usage. TaintDroid implements system‑wide dynamic taint tracking by instrumenting Android’s virtualized execution environment, enabling real‑time analysis of multiple sensitive data sources. Evaluation shows TaintDroid adds only 14% overhead on a CPU‑bound micro‑benchmark, negligible overhead on interactive apps, and uncovered 68 potential privacy violations in 30 popular Android apps, giving users and security firms actionable insights.

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.