cronokirby

(2026-08) DumboMix; Robust Asynchronous Anonymous Broadcast Made Practical

2026-08-15

Abstract

We present a practical framework DumboMix\mathsf{DumboMix} for asynchronous anonymous broadcasts with guaranteed output delivery (G.O.D., a.k.a. robustness), enabling a set of nn servers to privately solicit NN messages from distinct clients, such that these messages remain secret until they are simultaneously revealed in a uniformly random order. Here, asynchronous G.O.D. ensures that all solicited messages will eventually be randomly mixed despite (i) arbitrary malicious behaviors by up to n/3n/3 Byzantine servers and (ii) unpredictable network delays and jitters.

At the core of DumboMix\mathsf{DumboMix}, we first propose a couple of practical arithmetic circuits DumboMix1\mathsf{DumboMix1} and DumboMix2\mathsf{DumboMix2} for mixing in Shamir-secret-shared multi-party computation (MPC) over Zp\mathbb{Z}_p, along with their server-optimized variants. When randomly mixing NN messages, their online phases require only O(1)\mathcal{O}(1) multiplicative depth, expected O(N2)\mathcal{O}(N^2) scalar multiplications (between public and shared values), and up to O(N)\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 Blinder\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 O(\log2N)\mathcal{O}(\log^2 N) multiplicative depth; RabbitMix (Security’24) requires O(N2)\mathcal{O}(N^2) MPC multiplications; and PowerMix (CCS’19) incurs N3/2N^{3}/2 scalar multiplications.

We also implement our mixing methods within DumboMPC++\mathsf{DumboMPC\text{++}}, our computation-optimized implementation of the state-of-the-art robust AMPC framework DumboMPC\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=4n=4 to 3131 servers under varying network settings, revealing that our new mixing circuits 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).