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Anmol N. Sheth

dblp:149/7520 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 67% Privacy and data protection · 33%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › information flow tracking
dynamic taint analysis
0.212014
TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones · ACM Trans. Comput. Syst. 2014
Systems and software security
information flow tracking
0.212014
TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones · ACM Trans. Comput. Syst. 2014

Methods — techniques the papers use, named apart from their topics

runtime instrumentation · 0.2dynamic taint tracking · 0.2
YearPublicationVenuePosition
2014 TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones
abstract
Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share 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 enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32% performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.
William Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick D. McDaniel, Anmol N. Sheth
ACM Trans. Comput. Syst.9