Decentralized Reliability Estimation for Low Latency Mixnets

TL;DR

This paper introduces a decentralized scheme for estimating the reliability of links and nodes in low-latency mixnets, enabling scalable, verifiable, and efficient performance measurement without compromising latency or bandwidth.

Contribution

It presents a novel, scalable method for reliability estimation in mixnets using VRF-based routing and unforgeable measurement packets, addressing a key challenge in verifiable low-latency mixnets.

Findings

Feasibility demonstrated in unreliable settings

Effective in adversarial environments

Maintains low latency and bandwidth efficiency

Abstract

While there exist mixnets that can anonymously route large amounts of data packets with end to end latency that can be as low as a second, %making them attractive for a variety of applications, combining this level of performance with strong verifiability and reliability properties that ensure the correct processing and delivery of packets has proved challenging. Indeed, existing verifiability mechanisms are incompatible with scalable low-latency operation due to imposing significant latency overheads measuring in minutes to hours, hence severely limiting the variety of applications mixnets can serve. We address this important gap by proposing a scheme that can estimate reliability scores for a mixnet's links and nodes in a decentralized manner with essentially optimal complexity that is independent of the total traffic routed through the mixnet. The scores can be computed publicly by all participants from a set of measurement packets that are eventually revealed and act as a random sample of the traffic, without affecting mixnet transmission latency for client packets or incurring significant bandwidth overhead. Our scheme assumes client credentials and relies on VRF-based routing, a novel primitive that ensures that legitimate client packets follow the routing policy of the mixnet, as well as randomly generating unforgeable measurement packets. We experimentally validate our construction both in unreliable and adversarial settings, demonstrating its feasibility.