DAG-based Consensus with Asymmetric Trust [Extended Version]
Auteurs : Ignacio Amores-Sesar, Christian Cachin, Juan Villacis, Luca Zanolini
Résumé : In protocols with asymmetric trust, each participant is free to make its own individual trust assumptions about others, captured by an asymmetric quorum system. This contrasts with ordinary, symmetric quorum systems and with threshold models, where all participants share the same trust assumption. It is already known how to realize reliable broadcasts, shared-memory emulations, and binary consensus with asymmetric quorums. In this work, we introduce Directed Acyclic Graph (DAG)-based consensus protocols with asymmetric trust. To achieve this, we extend the key building-blocks of the well-known DAG-Rider protocol to the asymmetric model. Counter to expectation, we find that replacing threshold quorums with their asymmetric counterparts in the existing constant-round gather protocol does not result in a sound asymmetric gather primitive. This implies that asymmetric DAG-based consensus protocols, specifically those based on the existence of common-core primitives, need new ideas in an asymmetric-trust model. Consequently, we introduce the first asymmetric protocol for computing a common core, equivalent to that in the threshold model. This leads to the first randomized asynchronous DAG-based consensus protocol with asymmetric quorums. It decides within an expected constant number of rounds after an input has been submitted, where the constant depends on the quorum system.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.