Once is Never Enough: Foundations for Sound Statistical Inference in Tor Network Experimentation
Once is Never Enough: Foundations for Sound Statistical Inference in Tor Network Experimentation
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发表时间：
2021-02
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通讯作者：
Rob Jansen;J. Tracey;I. Goldberg
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作者：
Rob Jansen;J. Tracey;I. Goldberg
Tor是一种流行的低延迟匿名通信系统，专注于可用性和性能：更快的网络将吸引更多的用户，这反过来将提高使用该系统的每个人的匿名性。以前试图提高Tor性能的研究的标准做法是从标准Tor和每个研究变体的单个模拟的观察结果中得出结论。但由于模拟是在采样的Tor网络中运行的，因此采样误差本身可能会导致观察到的影响。因此，我们质疑在不考虑报告结果的统计学意义的情况下得出的任何结论的实际意义。在本文中，我们建立的基础上，我们改进的Tor实验方法。首先，我们提出了一种新的Tor网络建模方法，该方法可以生成更具代表性的Tor网络，以及新的和改进的实验工具，这些工具可以比以前更快地运行Tor模拟，并且规模更大。我们通过运行6，489个中继和792k同时活跃用户的模拟来展示这些贡献，这是已知最大的Tor网络模拟，也是第一个100%网络规模的模拟。其次，我们提出了新的统计方法，通过这些方法，我们：（i）表明在独立采样的网络中运行多个模拟是必要的，以便产生信息丰富的结果;（ii）表明如何使用多个模拟的结果进行合理的统计推断。我们提出了一个案例研究，使用420模拟演示如何将我们的方法应用到一组具体的Tor实验，以及如何分析结果。
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. 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. But because the simulations are run in sampled Tor networks, it is possible that sampling error alone could cause the observed effects. Therefore, we call into question the practical meaning of any conclusions that are drawn without considering the statistical significance of the reported results. In this paper, we build foundations upon which we improve the Tor experimental method. 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. 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%. 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 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.