  We propose and analyze a recipient-anonymous stochastic routing model to
study a fundamental trade-off between anonymity and routing delay. An agent
wants to quickly reach a goal vertex in a network through a sequence of routing
actions, while an overseeing adversary observes the agent's entire trajectory
and tries to identify her goal among those vertices traversed. We are
interested in understanding the probability that the adversary can correctly
identify the agent's goal (anonymity), as a function of the time it takes the
agent to reach it (delay). A key feature of our model is the presence of
intrinsic uncertainty in the environment, so that each of the agent's intended
steps is subject to random perturbation and thus may not materialize as
planned. Using large-network asymptotics, our main results provide near-optimal
characterization of the anonymity-delay trade-off under a number of network
topologies. Our main technical contributions are centered around a new class of
"noise-harnessing" routing strategies that adaptively combine intrinsic
uncertainty from the environment with additional artificial randomization to
achieve provably efficient obfuscation.
