Stopping Silent Sneaks: Defending against Malicious Mixes with Topological Engineering

Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performant, and scalable. In this paper, we focus our work on stratified Mixnets, a popular design with real-world adoption. We identify and measure significant impacts of practical aspects such as: relay sampling and topology placement, network churn, and risks due to real-world usage patterns. We show that, due to the lack of incorporating these aspects in design decisions, Mixnets of this type are far more susceptible to user deanonymization than expected. In order to reason about and resolve these issues, we model Mixnets as a three-stage “Sample-Placement-Forward” pipeline and develop tools to analyze and evaluate design decisions. To address the identified gaps and weaknesses we propose Bow-Tie, a design that mitigates user deanonymization through a novel adaption of Tor’s guard design with an engineered guard layer and client guard-logic for stratified mixnets. We show that Bow-Tie has significantly higher user anonymity in the dynamic setting, where the Mixnet is used over a period of time, and is no worse in the static setting, where the user only sends a single message. We show the necessity of both the guard layer and client guard-logic in tandem as well as their individual effect when incorporated into other reference designs. We develop and implement two tools, 1) a mixnet topology generator (Mixnet-Topology-Generator (MTG)) and 2) a path simulator and security evaluator (routesim) that takes into account temporal dynamics and user behavior, to assist our analysis and empirical data collection. These tools are designed to help Mixnet designers assess the security and performance impact of their design decisions.