Shining Light in Dark Places: Understanding the Tor Network

By Damon McCoy, Kevin S. Bauer, Dirk Grunwald, Tadayoshi Kohno, D. Sicker. International Symposium on Privacy Enhancing Technologies, 2008

367 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/97cd66eb1c75f2b5d4c91c7d2d6661b56d6988bc

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Waiting for Anonymity: Understanding Delays in the Tor Overlay (2010, background): “McCoy et al. [10] performed a measurement study concluding that the web traffic makes up most of the connections in Tor.”
Traveling the silk road: a measurement analysis of a large anonymous online marketplace (2012, methodology): “provide d a characterization of traffic using the Tor network by monitoring a Tor exit node [20].”
Security and Privacy in the Internet of Things (2017, background): “Two main reasons that slow down Tor are: (1) the path selection schemes designed for balancing traffic over the entire Tor network [133, 149], and (2) bulk traffic contending for bandwidth with interactive users [113, 139, 91].”
Vanish: Increasing Data Privacy with Self-Destructing Data (2009, background): “One could also use a different exit node for each share to counter potentially malicious Tor exit nodes [36, 66], or use Tor for only a subset of the shares.”
Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services (2015, neutral): “Furthermore, since Tor is mainly used for web browsing [29], we designed our datasets so that most of the instances are “general” circuits to reflect realistically what an adversary would face when attempting to classify circuits of real users on the live network.”
Website fingerprinting in onion routing based anonymization networks (2011, background): “Thereby, anonymization is the technical basis for a significant number of users living in oppressive regimes [15] giving users the opportunity to communicate freely and, under certain circumstances, to evade censorship.”
Distributed Measurement with Private Set-Union Cardinality (2017, background): “In the context of privacy-preserving systems, such as anonymity networks, this approach can be problematic, and attempts that directly measure user behavior in anonymity networks [37] have been heavily criticized.”
PrivEx: Private Collection of Traffic Statistics for Anonymous Communication Networks (2014, methodology): “The work by McCoy et al. [23] provided many insights about Tor client behaviour.”