Paper 2026/1699 Original: eprint.iacr.org/2026/1699 ↗

DumboMix: Robust Asynchronous Anonymous Broadcast Made Practical

Abstract

We present a practical framework $\mathsf{DumboMix}$ for asynchronous anonymous broadcasts with guaranteed output delivery (G.O.D., a.k.a. robustness), enabling a set of $n$ servers to privately solicit $N$ messages from distinct clients, such that these messages remain secret until they are revealed in uniform random order. Here, asynchronous G.O.D. ensures that all messages are eventually randomly mixed despite (i) arbitrary malicious behaviors by up to $\lfloor (n-1)/3 \rfloor$ Byzantine servers and (ii) unpredictable network delays and jitters. At the core of $\mathsf{DumboMix}$, we first propose a couple of practical mixing methods $\mathsf{DumboMix1}$ and $\mathsf{DumboMix2}$ for Shamir-secret-shared multi-party computation (MPC) over $\mathbb{Z}_p$, along with their server-optimized variants. When randomly mixing $N$ messages, their online phases require only $\mathcal{O}(1)$ multiplicative depth, expected $\mathcal{O}(N^2)$ scalar multiplications (between public and shared values), and up to $\mathcal{O}(N)$ MPC multiplications (between shared values). Moreover, assuming a robust underlying MPC framework, they guarantee that all revealed inputs are uniformly shuffled. In contrast, existing techniques fail to achieve all these performance and functionality features: The DC-net variant $\mathsf{Blinder}$ (CCS’20) may reveal a non-negligible fraction of inputs without shuffling them; Butterfly switching networks in secret-shared MPC (CCS’19) incur $\mathcal{O}(\log^2 N)$ multiplicative depth; RabbitMix (Security’24) requires $\mathcal{O}(N^2)$ MPC multiplications; and PowerMix (CCS’19) incurs $N^{3}/2$ scalar multiplications. We finally implement our new mixing methods within $\mathsf{DumboMPC\text{++}}$, our computation-optimized implementation of the state-of-the-art robust AMPC framework $\mathsf{DumboMPC}$ (Security’25), which provides more concretely efficient offline preprocessing while preserving asynchronous G.O.D. and optimal resilience. We then conduct extensive evaluations with $n=4$ to $31$ servers under varying network settings, revealing that our new mixing methods achieve 44.8--65.9× (resp. 37.1--52.7×), 4.8--7.1× (resp. 3.9--5.5×), and 2.7--4.0× (resp. 5.1--7.2×) speedups over RabbitMix, PowerMix, and the butterfly switching network, respectively, when shuffling 1024 messages in LAN (resp. WAN).