2026-07-22T16:01:40Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20939 2024-09-16T10:19:08Z ddc:004 open_access

CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols Tang, Weizhao Sheng, Peiyao Ni, Ronghao Roy, Pronoy Wang, Xuechao Fanti, Giulia Viswanath, Pramod CFT Protocols forensics blockchain Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted - e.g., enterprise settings and government infrastructure. However, CFT consensus can be broken by even a single corrupt node. A desirable property in the face of such potential Byzantine faults is accountability: if a corrupt node breaks the protocol and affects consensus safety, it should be possible to identify the culpable components with cryptographic integrity from the node states. Today, the best-known protocol for providing accountability to CFT protocols is called PeerReview; it essentially records a signed transcript of all messages sent during the CFT protocol. Because PeerReview is agnostic to the underlying CFT protocol, it incurs high communication and storage overhead. We propose CFT-Forensics, an accountability framework for CFT protocols. We show that for a special family of forensics-compliant CFT protocols (which includes widely-used CFT protocols like Raft and multi-Paxos), CFT-Forensics gives provable accountability guarantees. Under realistic deployment settings, we show theoretically that CFT-Forensics operates at a fraction of the cost of PeerReview. We subsequently instantiate CFT-Forensics for Raft, and implement Raft-Forensics as an extension to the popular nuRaft library. In extensive experiments, we demonstrate that Raft-Forensics adds low overhead to vanilla Raft. With 256 byte messages, Raft-Forensics achieves a peak throughput 87.8% of vanilla Raft at 46% higher latency (+44 ms). We finally integrate Raft-Forensics into the open-source central bank digital currency OpenCBDC, and show that in wide-area network experiments, Raft-Forensics achieves 97.8% of the throughput of Raft, with 14.5% higher latency (+326 ms). Schloss Dagstuhl – Leibniz-Zentrum für Informatik Weizhao Tang and Peiyao Sheng and Ronghao Ni and Pronoy Roy and Xuechao Wang and Giulia Fanti and Pramod Viswanath 2024 Is Part Of LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.AFT.2024.3 urn:nbn:de:0030-drops-209399 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.3 eng https://creativecommons.org/licenses/by/4.0/legalcode