TIGER: Tor Traffic Generator for Realistic Experiments

Abstract

Tor is the most widely adopted anonymity network, helping safeguard the privacy of Internet users, including journalists and human rights activists. However, effective attacks aimed at deanonymizing Tor users' remains a significant threat. Unfortunately, evaluating the impact such attacks by collecting realistic Tor traffic without gathering real users’ data poses a significant challenge.

This paper introduces TIGER (Tor traffIc GEnerator for Realistic experiments), a novel framework that automates the generation of realistic Tor traffic datasets towards improving our understanding of the robustness of Tor's privacy mechanisms. To this end, TIGER allows researchers to design large-scale testbeds and collect data on the live Tor network while responsibly avoiding the need to collect real users' traffic. We motivate the usefulness of TIGER by collecting a preliminary dataset with applicability to the evaluation of traffic confirmation attacks and defenses.