Since the Tor network is evolving into an infrastructure for anonymous communication, analyzing the consequences of network growth is becoming more relevant than ever. In particular, adding large amounts of resources may have unintentional consequences for the system performance as well as security. To this end, we contribute a methodology for the analysis of scaled Tor networks that enables researchers to leverage real-world network data. Based on historical network snapshots (consensuses), we derive and implement a model for methodically scaling Tor consensuses. This allows researchers to apply established research methods to scaled networks. We validate our model based on historical data, showing its applicability. Furthermore, we demonstrate the merits of our data-driven approach by conducting a simulation study to identify performance impacts of scaling Tor.

翻译：随着Tor网络逐渐演变为匿名通信基础设施，分析网络增长带来的影响比以往任何时候都更具现实意义。特别是大规模资源投入可能对系统性能及安全性产生意外后果。为此，我们提出一种用于分析扩展后Tor网络的方法论框架，使研究人员能够利用真实网络数据。基于历史网络快照（共识数据），我们推导并实现了一个系统化扩展Tor共识数据的模型。该模型使研究人员能够将既有的研究方法应用于扩展后的网络。我们基于历史数据对模型进行了验证，证明了其适用性。此外，通过开展模拟研究以识别网络扩展对性能的影响，我们展示了这种数据驱动方法的优势。