TY - GEN
T1 - Practical Settlement Bounds for Longest-Chain Consensus
AU - Gaži, Peter
AU - Ren, Ling
AU - Russell, Alexander
N1 - Acknowledgments. Anasuya Acharya and Carmit Hazay are supported by ISF grant No. 1316/18. Carmit Hazay is also supported by the Algorand Centres of Excellence programme managed by Algorand Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of Algorand Foundation. The fourth author was supported by a JPMorgan Chase Faculty Research Award, Technology, and Humanity Fund from the McCourt School of Public Policy at Georgetown University, and a Google Research Award.
Acknowledgements. Research supported in part by an Alon Young Faculty Fellowship, by a grant from the Israel Science Foundation (ISF Grant No. 1774/20), and by a grant from the US-Israel Binational Science Foundation and the US National Science Foundation (BSF-NSF Grant No. 2020643).
2055694. Vassilis Zikas’s research is supported in part by NSF grant no. 2055599 and by Sunday Group. The authors are also supported by the Algorand Centres of Excellence programme managed by Algorand Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of Algorand Foundation.
Grant. This material is based upon work supported by the Defense Advanced Research Projects Agency through Award HR00112020024. A. Srinivasan was supported in part by a SERB startup grant and Google India Research Award.
Acknowledgment. Y. Ishai was supported in part by ERC Project NTSC (742754), BSF grant 2018393, ISF grant 2774/20, and a Google Faculty Research Award. D. Khu-rana was supported in part by NSF CAREER CNS-2238718 and DARPA SIEVE. A. Sahai was supported in part from a Simons Investigator Award, DARPA SIEVE award, NTT Research, NSF Frontier Award 1413955, BSF grant 2012378, a Xerox Faculty Research Award, a Google Faculty Research Award, and an Okawa Foundation Research
tial privacy in the shuffle model and the anonymous reviewers for their comments. Y. Ishai and E. Kushilevitz were supported by ISF grant 2774/20 and BSF grant 2018393. Y. Ishai was additionally supported by ERC Project NTSC (742754).
Acknowledgments. Ran Cohen’s research is supported in part by NSF grant no. 2055568. Juan Garay’s research is supported in part by NSF grants no. 2001082 and
G. Garimella, M. Rosulek and J. Singh—Authors partially supported by NSF award S2356A.
Acknowledgements. D. Boneh is supported by NSF, the DARPA SIEVE program, the Simons Foundation, UBRI, and NTT Research. E. Boyle is supported by AFOSR Award FA9550-21-1-0046, ERC Project HSS (852952), and a Google Research Award. H. Corrigan-Gibbs is supported by Capital One, Facebook, Google, Mozilla, Seagate, MIT’s FinTech@CSAIL Initiative, and NSF Award CNS-2054869. N. Gilboa is supported by ISF grant 2951/20, ERC grant 876110, and a grant by the BGU Cyber Center. Y. Ishai is supported by ERC Project NTSC (742754), BSF grant 2018393, and ISF grant 2774/20. Opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.
Acknowledgments. The research described in this paper received funding from: the Concordium Blockhain Research Center, Aarhus University, Denmark; the Carlsberg Foundation under the Semper Ardens Research Project CF18-112 (BCM); the European Research Council (ERC) under the European Unions’s Horizon 2020 research and innovation programme under grant agreement No 803096 (SPEC); the Danish Independent Research Council under Grant-ID DFF-0165-00107B (C3PO).
ritos and quesadillas. He also thanks the Aarhus Crypto Group and the people at NTT Research for being amazing humans (independently of their success in research). The work of Damiano Abram was carried out during an internship funded by NTT Research.
Acknowledgements. We would like to thank Alin Tomescu, Kobi Gurkan, Julian Loss, and Renas Bacho for many insightful discussions. Gilad Stern was supported by the HUJI Federmann Cyber Security Research Center in conjunction with the Israel National Cyber Directorate (INCD) in the Prime Minister’s Office.
Acknowledgements. This work is supported in part by DARPA under Cooperative Agreement HR0011-20-2-0025, the Algorand Centers of Excellence programme managed by Algorand Foundation, NSF grants CNS-2246355, CCF-2220450 and CNS-2001096, US-Israel BSF grant 2015782, Amazon Faculty Award, Cisco Research Award and Sunday Group. Any views, opinions, findings, conclusions or recommendations contained herein are those of the author(s) and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, the Department of Defense, the Algorand Foundation, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes not withstanding any copyright annotation therein.
Acknowledgements. Pedro Branco was partially funded by the German Federal Ministry of Education and Research (BMBF) in the course of the 6GEM research hub under grant number 16KISK038. Nico Döttling: Funded by the European Union (ERC, LACONIC, 101041207). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Akshayaram Srinivasan was supported in part by a SERB startup grant and Google India Research Award.
Acknowledgements. This work is funded in part by National Science Foundation award 2143058.
PY - 2023
Y1 - 2023
N2 - Nakamoto’s longest-chain consensus paradigm now powers the bulk of the world’s cryptocurrencies and distributed finance infrastructure. An emblematic property of longest-chain consensus is that it provides probabilistic settlement guarantees that strengthen over time. This makes the exact relationship between settlement error and settlement latency a critical aspect of the protocol that both users and system designers must understand to make informed decisions. A recent line of work has finally provided a satisfactory rigorous accounting of this relationship for proof-of-work longest-chain protocols, but those techniques do not appear to carry over to the proof-of-stake setting. This article develops a new analytic approach for establishing such settlement guarantees that yields explicit, rigorous settlement bounds for proof-of-stake longest-chain protocols, placing them on equal footing with their proof-of-work counterparts. Our techniques apply with some adaptations to the proof-of-work setting where they provide improvements to the state-of-the-art settlement bounds for proof-of-work protocols.
AB - Nakamoto’s longest-chain consensus paradigm now powers the bulk of the world’s cryptocurrencies and distributed finance infrastructure. An emblematic property of longest-chain consensus is that it provides probabilistic settlement guarantees that strengthen over time. This makes the exact relationship between settlement error and settlement latency a critical aspect of the protocol that both users and system designers must understand to make informed decisions. A recent line of work has finally provided a satisfactory rigorous accounting of this relationship for proof-of-work longest-chain protocols, but those techniques do not appear to carry over to the proof-of-stake setting. This article develops a new analytic approach for establishing such settlement guarantees that yields explicit, rigorous settlement bounds for proof-of-stake longest-chain protocols, placing them on equal footing with their proof-of-work counterparts. Our techniques apply with some adaptations to the proof-of-work setting where they provide improvements to the state-of-the-art settlement bounds for proof-of-work protocols.
UR - https://www.scopus.com/pages/publications/85172322961
UR - https://www.scopus.com/pages/publications/85172322961#tab=citedBy
U2 - 10.1007/978-3-031-38557-5_4
DO - 10.1007/978-3-031-38557-5_4
M3 - Conference contribution
AN - SCOPUS:85172322961
SN - 9783031385568
T3 - Lecture Notes in Computer Science
SP - 107
EP - 138
BT - Advances in Cryptology – CRYPTO 2023 - 43rd Annual International Cryptology Conference, CRYPTO 2023, Proceedings
A2 - Handschuh, Helena
A2 - Lysyanskaya, Anna
PB - Springer
T2 - 43rd Annual International Cryptology Conference, CRYPTO 2023
Y2 - 20 August 2023 through 24 August 2023
ER -