D2Taint: Differentiated and dynamic information flow tracking on smartphones for numerous data sources

Abstract

Abstract-With smartphones' meteoric growth in recent years, leaking sensitive information from them has become an increasingly critical issue. Such sensitive information can originate from smartphones themselves (e.g., location information) or from many Internet sources (e.g., bank accounts, emails). While prior work has demonstrated information flow tracking's (IFT's) effectiveness at detecting information leakage from smartphones, it can only handle a limited number of sensitive information sources. This paper presents a novel IFT tagging strategy using differentiated and dynamic tagging. We partition information sources into differentiated classes and store them in fixed-length tags. We adjust tag structure based on time-varying received information sources. Our tagging strategy enables us to track at runtime numerous information sources in multiple classes and rapidly detect information leakage from any of these sources. We design and implement D2Taint, an IFT system using our tagging strategy on real-world smartphones. We experimentally evaluate D2Taint's effectiveness with 84 real-world applications downloaded from Google Play. D2Taint reports that over 80% of them leak data to third-party destinations; 14% leak highly sensitive data. Our experimental evaluation using a standard benchmark tool illustrates D2Taint's effectiveness at handling many information sources on smartphones with moderate runtime and space overhead.

Towards Understanding the Advertiser's Perspective of Smartphone User Privacy

“…RELATED WORK In response to the widespread popularity of smartphones, much attention has been paid in studying application security and user privacy related issues. Extensive security analysis on smartphone apps has been carried out and can be categorized into permission analysis, static analysis, and dynamic analysis [3], [5], [6], [10], [13], [15], [26]- [31].…”

“…TaintDroid [3] tracks the flow of privacy sensitive data and reports when sensitive data leaves the system via interfaces such as network connections. D2Taint [15] tracks detailed private leakage sources at runtime in multiple classes and detects leakages from any of these sources rapidly. Agarwal et al [11] proposes ProtectMyPrivacy (PMP) which utilizes a crowdsourcing based mechanism to help users decide proper privacy settings for iOS apps.…”

“…Existing study [4]- [6], [9], [12]- [14] is mostly on the static aspects of apps' permissions. TaintDroid [3] and D2Taint [15] can be used to log the leaking activities but the papers did not focus on a systematic study on the destinations, frequencies and types of apps' run-time privacy leakages. Second, the consequences of such leakage, especially when an advertiser gathers such private data from many users and across many apps, is not known either.…”

PAGURUS: Low-Overhead Dynamic Information Flow Tracking on Loosely Coupled Accelerators

“…DIFT has been shown to be effective in protecting systems against several softwarebased attacks, including leakage of information [23] and code injection [24]. DIFT is now implemented on different types of architectures [19], [25], including smartphones [23], [26]. Most of the approaches on hardware-based DIFT focused only on securing processor cores and the associated logic, i.e., tightly coupled accelerators, memories and communication channels.…”

Information flow policies vs malware

“…In [2] Enck et al presented TaintDroid and exposed the threat, proving by example, that more than a third of the most popular applications available on Google Play are responsible for sensitive information leakage. More recently Gu et al presented in [3] D2taint a system able to track system data flows for Java code at the variable level, similar to 1 what TaintDroid does. D2taint goes further than TaintDroid since its tagging strategy permits numerous information sources in multiple classes at runtime.…”