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CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols

Authors Weizhao Tang , Peiyao Sheng , Ronghao Ni , Pronoy Roy, Xuechao Wang , Giulia Fanti , Pramod Viswanath

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ACM Subject Classification
  • Security and privacy → Distributed systems security
  • Networks → Security protocols
Keywords
  • CFT Protocols
  • forensics
  • blockchain

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Abstract

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).

Cite As Get BibTex

Weizhao Tang, Peiyao Sheng, Ronghao Ni, Pronoy Roy, Xuechao Wang, Giulia Fanti, and Pramod Viswanath. CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 3:1-3:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.AFT.2024.3

Author Details

Weizhao Tang
  • Carnegie Mellon University, Pittsburgh, PA, USA
Peiyao Sheng
  • University of Illinois Urbana-Champaign, IL, USA
Ronghao Ni
  • Carnegie Mellon University, Pittsburgh, PA, USA
Pronoy Roy
  • Carnegie Mellon University, Pittsburgh, PA, USA
Xuechao Wang
  • Hong Kong University of Science and Technology, Guangzhou, China
Giulia Fanti
  • Carnegie Mellon University, Pittsburgh, PA, USA
Pramod Viswanath
  • Princeton University, NJ, USA

Funding

Weizhao Tang, Ronghao Ni, Pronoy Roy and Giulia Fanti: This work was supported in part by the National Science Foundation under grants CNS-2325477, CIF-1705007, and CCF-2338772, and the Air Force Office of Scientific Research under award number FA9550-21-1-0090. We also thank Chainlink Labs, Ripple Labs, and IC3 industry partners for their generous support, as well as Bosch, the Sloan Foundation, Intel, and the CyLab Secure Blockchain Initiative.
  • Wang, Xuechao: This work is supported in part by a gift from Stellar Development Foundation and by the Guangzhou-HKUST(GZ) Joint Funding Program (No. 2024A03J0630).
  • Viswanath, Pramod: This work is supported in part by NSF CNS-2325477, ARO W911NF2310147 and C3.AI.

Acknowledgements

We wish to thank Chris Meiklejohn and Heather Miller for their valuable insights and advice on this project. We also thank Sam Stuewe and the MIT Digital Currency Initiative for their feedback and insights regarding integration with OpenCBDC and applications to central bank digital currency.

Supplementary Materials

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