Please note: This master’s thesis presentation will take place online.
Ivy Vecna, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ian Goldberg
Tor is an important tool for protecting people against Internet surveillance and censorship. Governments that wish to control their people may thus attempt to block access to Tor. Bridges are circumvention proxies that provide routes around this censorship, enabling people to access Tor, even in countries that ordinarily censor it. However, a motivated censor may work to identify these bridges and block access to them.
To impede the censor’s attempts at identifying and blocking bridges, reputation-based bridge distribution systems such as Lox have been proposed. These systems place greater trust in users when the bridges they know remain uncensored and reduced trust in users when bridges they know become censored. In order to enact these changes in trust, it is necessary to know which bridges have been blocked and which have not.
In this work, we present Troll Patrol, a system for automatically detecting censorship of Tor bridges. This system infers bridge reachability based on already-existing bridge usage statistics and novel anonymous user reports that we design for this purpose. We evaluate our system using a simulation and demonstrate that user reports improve our ability to detect bridge censorship, compared to using statistics on bridge use alone. We describe an attack that allows the censor to evade detection if classification of bridge blockage relies on bridge statistics alone, and we demonstrate that user reports allow us to defend against this attack.