We analyze the anonymity provided by continuous mixnets (e.g., Loopix) when messages with different latency requirements are sent through the same network. The anonymity provided by existing mixnets that offer bounded latency guarantees has only been studied considering that all the traffic in the network follows the same latency distribution. In this work we evaluate whether it is beneficial to aggregate different types of traffic in the same network or to keep them separate, when the latency distributions are exponential and the traffic arrivals are a poisson process --- as is the case in Loopix and related designs. We present a novel evaluation method to analyze the leakage to the adversary when multiple different types of traffic are sent through the same network of continuous mixes. We apply the method to empirically evaluate the end-to-end anonymity (in terms of entropy) for each type of traffic in the presence of a global passive adversary that may additionally compromise a constant fraction of mixes or may have knowledge about the type of traffic of network output messages. Finally we show via empirical evaluation using our analytical framework that it is beneficial for anonymity to blend different types of traffic in the same mixnet.

We propose a method to optimally select mix network parameters for a given deployment context and adversarial model. Our method considers both worstcase and average-case anonymity and selects configurations that meet worst-case constraints while maximizing average anonymity. We apply our methods to mixnet size optimization to determine the number and width of mixnet layers, and provide results for various deployment and adversarial scenarios. For cases where the deployment context suddenly changes (drop in user traffic) we evaluate countermeasures based on mix-generated dummy traffic and show that inexpensive link dummies can significantly boost protection in some of these cases.