🤖 AI Summary
Traditional CRDTs struggle to ensure state consistency in Byzantine environments due to their reliance on update filtering mechanisms. This work proposes a deterministic state reconstruction approach that decouples update propagation from state derivation: all updates are accepted, but only valid ones contribute to the final state. By structurally rejecting or transforming malicious updates, the system guarantees convergence of replicas under arbitrary update injection and supports a layered security model encompassing authentication, authorization, and confidentiality. Built upon delta-state CRDTs, the resulting Melda system is tailored for JSON documents and formally verified to preserve state consistency despite message reordering, loss, or forgery. Theoretical analysis confirms that identical update sets yield indistinguishable states, achieving coordination-free strong eventual consistency with Byzantine fault tolerance.
📝 Abstract
Conflict-free Replicated Data Types (CRDTs) ensure Strong Eventual Consistency without coordination, but typically assume benign participants and rely on validation or exclusion to handle Byzantine behavior.
We address this problem through deterministic state reconstruction: rather than deciding which updates are admissible, all accepted updates are incorporated, while only a subset contributes to the reconstructed state. We instantiate this approach in Melda, a non-intrusive delta-state CRDT for JSON documents, and show that its reconstruction model guarantees convergence even under arbitrary update injection: adversarial updates are either structurally rejected or treated as inputs to the reconstruction process.
We formalize this model and prove that replicas deriving state from the same set of updates cannot diverge despite equivocation, omission, or message reordering. We further show that authentication, authorization, and confidentiality can be layered without affecting convergence.
Overall, this approach suggests that Byzantine tolerance can be achieved by decoupling update propagation from state derivation, allowing agreement on updates to be handled independently by external dissemination or consensus mechanisms.