MOCHA: Mixnet Optimization Considering Honest Client Anonymity

Mix networks (mixnets) safeguard client anonymity by forwarding traffic through multiple intermediary nodes (mixnodes), which reorder and delay messages to obscure communication patterns against a global passive adversary capable of monitoring all network transmissions.The anonymity provided by mixnets is usually assessed with a discrete-event simulator, gauging a target message's indistinguishability among output messages.While useful for comparative analysis, this approach only approximates the mixnet's anonymity potential.Hence, this paper sheds light on the necessity of considering the client (originator of messages) itself to gauge anonymity accurately.We further provide an algorithm (simulator) to simulate client anonymity for Loopix mixnets.We conduct experiments to optimize general Loopix mixnet parameters, considering both message and client anonymity.Our findings indicate that message anonymity often provides an upper bound and can yield misleading results for mixnet optimization, underscoring the importance of client anonymity.Additionally, we explore scenarios where client anonymity is significantly compromised due to an insufficient number of clients.To address these cases, we propose a multimixing strategy that enhances client anonymity by effectively merging varied traffic types with different mixing characteristics.