Modeling Tor Network Growth by Extrapolating Consensus Data

Abstract

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.