Demo Data from EDBT 2026 / reviewers in the wild / expert
Anmol N. Sheth
dblp:149/7520
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › information flow tracking
dynamic taint analysis |
0.2 | 1 | 2014 | TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones · ACM Trans. Comput. Syst. 2014 |
Systems and software security
information flow tracking |
0.2 | 1 | 2014 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on SmartphonesabstractToday’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 |