![]() Windows users should click the purple Download tor Browser button and Mac users should click the green download text link to the right of the purple. We present a case study using 420 simulations to demonstrate how to apply our methodologies to a concrete set of Tor experiments and how to analyze the results. This new scheduler can make Tor browser traffic faster by determining how much data is sent through the Onion’s relays. Second, we present new statistical methodologies through which we: (i) show that running multiple simulations in independently sampled networks is necessary in order to produce informative results and (ii) show how to use the results from multiple simulations to conduct sound statistical inference. We showcase these contributions by running simulations with 6,489 relays and 792k simultaneously active users, the largest known Tor network simulations and the first at a network scale of 100%. First, we present a new Tor network modeling methodology that produces more representative Tor networks as well as new and improved experimentation tools that run Tor simulations faster and at a larger scale than was previously possible. ![]() In this paper, we build foundations upon which we improve the Tor experimental method. ![]() Therefore, we call into question the practical meaning of any conclusions that are drawn without considering the statistical significance of the reported results. ![]() But because the simulations are run in sampled Tor networks, it is possible that sampling error alone could cause the observed effects. The standard practice for previous research attempting to enhance Tor performance is to draw conclusions from the observed results of a single simulation for standard Tor and for each research variant. Tor is a popular low-latency anonymous communication system that focuses on usability and performance: a faster network will attract more users, which in turn will improve the anonymity of everyone using the system. ![]()
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