Nakamoto consensus is prevailing in the world largest blockchain-based cryptocurrency systems, such as Bitcoin and Ethereum. Since then, various attempts have been studied to attack Nakamoto consensus worldwide. In recent years, network delay has won more attention for making inconsistent ledgers in blockchain-based applications by virtue of attacking Nakamoto consensus. However, so far as we know, most of the existing works mainly focus on constructing inconsistent ledgers for blockchain systems, but not offering fine-grained theoretical analysis for how to optimize the success probability by flexibly dividing computational power and network delay from the viewpoint of adversary. The paper first utilizes network delay and the partition of controlled computation power of honest miners for making forks as long as possible. Then, formally analysis is provided to show the success probability of the proposed attack, and compute the optimal network delay and splitting for adversarial computation power in theory. Finally, simulation experiments validate the correctness of the formal analysis.
Citation: Shaochen Lin, Xuyang Liu, Xiujuan Ma, Hongliang Mao, Zijian Zhang, Salabat Khan, Liehuang Zhu. The impact of network delay on Nakamoto consensus mechanism[J]. Electronic Research Archive, 2022, 30(10): 3735-3754. doi: 10.3934/era.2022191
Nakamoto consensus is prevailing in the world largest blockchain-based cryptocurrency systems, such as Bitcoin and Ethereum. Since then, various attempts have been studied to attack Nakamoto consensus worldwide. In recent years, network delay has won more attention for making inconsistent ledgers in blockchain-based applications by virtue of attacking Nakamoto consensus. However, so far as we know, most of the existing works mainly focus on constructing inconsistent ledgers for blockchain systems, but not offering fine-grained theoretical analysis for how to optimize the success probability by flexibly dividing computational power and network delay from the viewpoint of adversary. The paper first utilizes network delay and the partition of controlled computation power of honest miners for making forks as long as possible. Then, formally analysis is provided to show the success probability of the proposed attack, and compute the optimal network delay and splitting for adversarial computation power in theory. Finally, simulation experiments validate the correctness of the formal analysis.
| [1] | S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system, Decentralized Bus. Rev., (2008), 21260. Available from: https://people.eecs.berkeley.edu/raluca/cs261-f15/readings/bitcoin.pdf. |
| [2] | J. Garay, A. Kiayias, N. Leonardos, The bitcoin backbone protocol: Analysis and applications, in Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, Berlin, Heidelberg, 9057 (2015), 281–310. https://doi.org/10.1007/978-3-662-46803-6_10 |
| [3] |
C. Dwork, N. Lynch, L. Stockmeyer, Consensus in the presence of partial synchrony, J. ACM, 35 (1988), 288–323. https://doi.org/10.1145/42282.42283 doi: 10.1145/42282.42283
|
| [4] | C. Decker, R. Wattenhofer, Information propagation in the bitcoin network, in IEEE P2P 2013 Proceedings, IEEE, (2013), 1–10. https://doi.org/10.1109/P2P.2013.6688704 |
| [5] | R. Pass, L. Seeman, A. Shelat, Analysis of the blockchain protocol in asynchronous networks, in Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, 10211 (2017), 643–673. https://doi.org/10.1007/978-3-319-56614-6_22 |
| [6] | P. Wei, Q. Yuan, Y. Zheng, Security of the blockchain against long delay attack, in International Conference on the Theory and Application of Cryptology and Information Security, Springer, 11274 (2018), 250–275. https://doi.org/10.1007/978-3-030-03332-3_10 |
| [7] |
Q. Yuan, P. Wei, K. Jia, H. Xue, Analysis of blockchain protocol against static adversarial miners corrupted by long delay attackers, Sci. China Inf. Sci., 63 (2020), 1–15. https://doi.org/10.1007/s11432-019-9916-5 doi: 10.1007/s11432-019-9916-5
|
| [8] | L. Shi, T. Wang, J. Li, S. Zhang, Pooling is not favorable: Decentralize mining power of pow blockchain using age-of-work, preprint, arXiv: 2104.01918. |
| [9] |
C. Li, B. Palanisamy, Comparison of decentralization in dpos and pow blockchains, Lect. Notes Comput. Sci., 12404 (2020). https://doi.org/10.1007/978-3-030-59638-5_2 doi: 10.1007/978-3-030-59638-5_2
|
| [10] | J. Bhosale, S. Mavale, Volatility of select crypto-currencies: A comparison of bitcoin, ethereum and litecoin, Annu. Res. J. SCMS, Pune, 6 (2018). Available from: https://www.scmspune.ac.in/journal/pdf/current/Paper%2010%20-%20Jaysing%20Bhosale.pdf. |
| [11] |
H. Chen, M. Pendleton, L. Njilla, S. Xu, A survey on ethereum systems security: Vulnerabilities, attacks, and defenses, ACM Comput. Surv., 53 (2020), 1–43. https://doi.org/10.1145/3391195 doi: 10.1145/3391195
|
| [12] | R. Pass, A. Shelat, Micropayments for decentralized currencies, in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, (2015), 207–218. https://doi.org/10.1145/2810103.2813713 |
| [13] | J. Poon, T. Dryja, The bitcoin lightning network: Scalable off-chain instant payments, 2016. Available from: https://lightning.network/lightning-network-paper.pdf. |
| [14] |
K. Gai, Y. Wu, L. Zhu, M. Qiu, M. Shen, Privacy-preserving energy trading using consortium blockchain in smart grid, IEEE Trans. Ind. Inf., 15 (2019), 3548–3558. https://doi.org/10.1109/TII.2019.2893433 doi: 10.1109/TII.2019.2893433
|
| [15] |
L. Zhu, Y. Wu, K. Gai, K. Choo, Controllable and trustworthy blockchain-based cloud data management, Future Gener. Comput. Syst., 91 (2018), 527–535. https://doi.org/10.1016/j.future.2018.09.019 doi: 10.1016/j.future.2018.09.019
|
| [16] |
K. Gai, Y. Wu, L. Zhu, Z. Zhang, M. Qiu, Differential privacy-based blockchain for industrial internet-of-things, IEEE Trans. Ind. Inf., 16 (2020), 4156–4165. https://doi.org/10.1109/TII.2019.2948094 doi: 10.1109/TII.2019.2948094
|
| [17] |
G. Xu, J. Dong, C. Ma, J. Liu, U. G. O. Cliff, A certificateless signcryption mechanism based on blockchain for edge computing, IEEE Internet Things J., 2022 (2022). https://doi.org/10.1109/JIOT.2022.3151359 doi: 10.1109/JIOT.2022.3151359
|
| [18] | I. Bentov, R. Kumaresan, How to use bitcoin to design fair protocols, Lect. Notes Comput. Sci., Springer, Berlin, Heidelberg, 8617 (2014), 421–439. https://doi.org/10.1007/978-3-662-44381-1_24 |
| [19] |
K. Gai, J. Guo, L. Zhu, S. Yu, Blockchain meets cloud computing: A survey, IEEE Commun. Surv. Tutorials, 22 (2020), 2009–2030. https://doi.org/10.1109/COMST.2020.2989392 doi: 10.1109/COMST.2020.2989392
|
| [20] | F. Yang, J. Tian, T. Feng, F. Xu, C. Qiu, C. Zhao, Blockchain-enabled parallel learning in industrial edge-cloud network: A fuzzy dpost-pbft approach, in 2021 IEEE Globecom Workshops (GC Wkshps), (2021), 1–6. https://doi.org/10.1109/GCWkshps52748.2021.9681977 |
| [21] |
G. Xu, Y. Liu, P. W. Khan, Improvement of the dpos consensus mechanism in blockchain based on vague sets, IEEE Trans. Ind. Inf., 16 (2020), 4252–4259. https://doi.org/10.1109/TII.2019.2955719 doi: 10.1109/TII.2019.2955719
|
| [22] |
Y. Liu, G. Xu, Fixed degree of decentralization dpos consensus mechanism in blockchain based on adjacency vote and the average fuzziness of vague value, Comput. Networks, 199 (2021), 108432. https://doi.org/10.1016/j.comnet.2021.108432 doi: 10.1016/j.comnet.2021.108432
|
| [23] | K. M. Elleithy, D. Blagovic, W. Cheng, P. Sideleau, Denial of service attack techniques: Analysis, implementation and comparison, J. Syst. Cybern. Inf., 3 (2005), 66–71. Available from: https://www.researchgate.net/publication/242497142. |
| [24] | E. Heilman, A. Kendler, A. Zohar, S. Goldberg, Eclipse attacks on bitcoin's peer-to-peer network, in Proceedings of the 24th USENIX Conference on Security Symposium, (2015), 129–144. Available from: https://eprint.iacr.org/2015/263.pdf. |
| [25] | M. Apostolaki, A. Zohar, L. Vanbever, Hijacking bitcoin: Routing attacks on cryptocurrencies, in 2017 IEEE Symposium on Security and Privacy (SP), IEEE, 2017 (2017), 375–392. https://doi.org/10.1109/SP.2017.29 |
| [26] | J. Bonneau, E. W. Felten, S. Goldfeder, J. A. Kroll, A. Narayanan, Why buy when you can rent? Bribery attacks on bitcoin consensus. Available from: https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C77871712694860A06EFCEC0665234B6?doi=10.1.1.698.738&rep=rep1&type=pdf. |
| [27] | G. O. Karame, E. Androulaki, S. Capkun, Double-spending fast payments in bitcoin, in Proceedings of the 2012 ACM Conference on Computer and Communications Security, (2012), 906–917. https://doi.org/10.1145/2382196.2382292 |
| [28] | I. Eyal, E. G. Sirer, Majority is not enough: Bitcoin mining is vulnerable, Lect. Notes Comput. Sci., Springer, Berlin, Heidelberg, 8437 (2013). https://doi.org/10.1007/978-3-662-45472-5_28 |
| [29] |
J. Göbel, H. P. Keeler, A. E. Krzesinski, P. G. Taylor, Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay, Perform. Eval., 104 (2016), 23–41. https://doi.org/10.1016/j.peva.2016.07.001 doi: 10.1016/j.peva.2016.07.001
|
| [30] | Y. Sompolinsky, A. Zohar, Secure high-rate transaction processing in bitcoin, in International Conference on Financial Cryptography and Data Security, Springer, Berlin, Heidelberg, 8975 (2015), 507–527. https://doi.org/10.1007/978-3-662-47854-7_32 |
Figures(2) / Tables(3)
Shaochen Lin, Xuyang Liu, Xiujuan Ma, Hongliang Mao, Zijian Zhang, Salabat Khan, Liehuang Zhu. The impact of network delay on Nakamoto consensus mechanism[J]. Electronic Research Archive, 2022, 30(10): 3735-3754. doi: 10.3934/era.2022191
DownLoad: