Algorand: Scaling Byzantine Agreements for Cryptocurrencies
Open accessAbstract
Algorand is a new cryptocurrency that confirms transactions with latency on the order of a minute while scaling to many users. Algorand ensures that users never have divergent views of confirmed transactions, even if some of the users are malicious and the network is temporarily partitioned. In contrast, existing cryptocurrencies allow for temporary forks and therefore require a long time, on the order of an hour, to confirm transactions with high confidence.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the reputation-based consistent hashing protocol, the probability of a party being selected is proportional to its reputation.
“The principle of the above process is similar to that of Algorand [27]: the probability of a party being selected is proportional to its reputation.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [27]
The security of the consensus protocol is equivalent to that of Algorand.
“so its security is equivalent to that of Algorand.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [27]
The proposed consensus protocol is improved on Algorand.
“our protocol is improved on Algorand [27].”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [27]
The consensus protocol follows Algorand, requiring that the newly generated block can only be identified as valid and broadcasted after it is approved by more than 2/3 of the verifiers.
“The consensus protocol follows Algorand [27], requiring that the newly generated block can only be identified as valid and broadcasted after it is approved by more than 2/3 of the verifiers”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Identity-Based Encryption from the Weil Pairing
Boneh, D., & Franklin, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44647-8_13 · Cited by 7,090 · see citing works
The Evolution of Random Graphs
Bollobás, B. (1986) · Regional conference series in mathematics · doi:10.1090/cbms/062/06 · Cited by 5,719 · see citing works
Douceur, J. R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45748-8_24 · Cited by 4,419 · see citing works
Tor: The Second-Generation Onion Router
Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104 · see citing works
Lamport, L. (1998) · ACM Transactions on Computer Systems · doi:10.1145/279227.279229 · Cited by 2,748 · see citing works
Practical byzantine fault tolerance and proactive recovery
Castro, M., & Liskov, B. (2002) · ACM Transactions on Computer Systems · doi:10.1145/571637.571640 · Cited by 2,475 · see citing works
Formalizing and Securing Relationships on Public Networks
Szabo, N. (1997) · First Monday · doi:10.5210/fm.v2i9.548 · Cited by 2,158 · see citing works
On the evolution of random graphs
Erdös, P., & Rényi, A. (2011) · The Structure and Dynamics of Networks · doi:10.1515/9781400841356.38 · Cited by 1,757 · see citing works
Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol
Kiayias, A., Russell, A. C., David, B., & Oliynykov, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63688-7_12 · Cited by 1,505 · see citing works
Majority Is Not Enough: Bitcoin Mining Is Vulnerable
Eyal, I., & Sirer, E. G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-45472-5_28 · Cited by 1,410 · see citing works
Information propagation in the Bitcoin network
Decker, C., & Wattenhofer, R. P. (2013) · IEEE International Conference on Peer-to-Peer Computing (P2P) · doi:10.1109/p2p.2013.6688704 · Cited by 1,224 · see citing works
Bitcoin-NG: A Scalable Blockchain ProtocolPreprint
Eyal, I., Gencer, A. E., Sirer, E. G., & van Renesse, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.02037 · Cited by 1,055 · see citing works
Curve25519: New Diffie-Hellman speed records
Bernstein, D. J. (2006) · Lecture Notes in Computer Science · doi:10.1007/11745853_14 · Cited by 859 · see citing works
The Honey Badger of BFT Protocols
Miller, A. K., Xia, Y., Croman, K., Shi, E., & Song, D. X. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978399 · Cited by 833 · see citing works
Micali, S., Rabin, M. O., & Vadhan, S. (2003) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sffcs.1999.814584 · Cited by 725 · see citing works
Self-citationSecure High-Rate Transaction Processing in Bitcoin
Sompolinsky, Y., & Zohar, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-47854-7_32 · Cited by 717 · see citing works
Proof of activity: Extending bitcoin’s proof of work via proof of stake [extended abstract] y
Bentov, I., Lee, C., Mizrahi, A., & Rosenfeld, M. (2014) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2695533.2695545 · Cited by 592 · see citing works
Eclipse attacks on Bitcoin's peer-to-peer network
Heilman, E., Kendler, A., Zohar, A., & Goldberg, S. (2015) · Cited by 577 · see citing works
Making Byzantine fault tolerant systems tolerate Byzantine faults
Clement, A., Wong, E. L., Alvisi, L., Dahlin, M., & Marchetti, M. (2009) · Cited by 381 · see citing works
Secure and Efficient Asynchronous Broadcast Protocols
Cachin, C., Kursawe, K., Petzold, F., et al. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44647-8_31 · Cited by 372 · see citing works
Fault-scalable Byzantine fault-tolerant services
Abd-El-Malek, M., Ganger, G. R., Goodson, G. R., Reiter, M. K., & Wylie, J. J. (2005) · ACM SIGOPS Operating Systems Review · doi:10.1145/1095809.1095817 · Cited by 369 · see citing works
Cryptocurrencies Without Proof of Work
Bentov, I., Gabizon, A., & Mizrahi, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53357-4_10 · Cited by 350 · see citing works
How to Use Bitcoin to Design Fair Protocols
Bentov, I., & Kumaresan, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44381-1_24 · Cited by 345 · see citing works
Kotla, R., Alvisi, L., Dahlin, M., Clement, A., & Wong, E. L. (2009) · ACM Transactions on Computer Systems · doi:10.1145/1658357.1658358 · Cited by 292 · see citing works
SPECTRE: A Fast and Scalable Cryptocurrency Protocol.Preprint
Sompolinsky, Y., Lewenberg, Y., & Zohar, A. (2016) · IACR Cryptology ePrint Archive · Cited by 243 · see citing works
Hybrid Consensus: Efficient Consensus in the Permissionless Model
Pass, R., & Shi, E. (2017) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.disc.2017.39 · Cited by 239 · see citing works
Proof of Activity: Extending Bitcoin's Proof of Work via Proof of StakePreprint
Bentov, I., Lee, C., Mizrahi, A., & Rosenfeld, M. (2014) · IACR Cryptology ePrint Archive · Cited by 214 · see citing works
Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem
Vasek, M., Thornton, M. A., & Moore, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44774-1_5 · Cited by 210 · see citing works
Identity-Based Encryption from the Diffie-Hellman Assumption
Döttling, N., & Garg, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63688-7_18 · Cited by 156 · see citing works
Extending binary Byzantine agreement to multivalued Byzantine agreement
Turpin, R., & Coan, B. (1984) · Information Processing Letters · doi:10.1016/0020-0190(84)90027-9 · Cited by 148 · see citing works
Fault-scalable Byzantine fault-tolerant services
Abd-El-Malek, M., Ganger, G. R., Goodson, G. R., Reiter, M. K., & Wylie, J. J. (2005) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/1095810.1095817 · Cited by 133 · see citing works
Beyond one-third faulty replicas in byzantine fault tolerant systems
Li, J., & Mazières, D. (2007) · Networked Systems Design and Implementation · Cited by 96 · see citing works
The diameter of sparse random graphs
Riordan, O. M., & Wormald, N. (2010) · Cited by 61 · see citing works
The Diameter of Sparse Random Graphs
RIORDAN, O., & WORMALD, N. (2010) · Combinatorics Probability Computing · doi:10.1017/s0963548310000325 · Cited by 48 · see citing works
On Generic Constructions of Circularly-Secure, Leakage-Resilient Public-Key Encryption Schemes
Hajiabadi, M., Kapron, B. M., & Srinivasan, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-49387-8_6 · Cited by 42 · see citing works
NSEC5 from Elliptic Curves: Provably Preventing DNSSEC Zone Enumeration with Shorter Responses.Preprint
Goldberg, S., Naor, M., Papadopoulos, D., et al. (2016) · IACR Cryptology ePrint Archive · Cited by 14 · see citing works
Very Simple and Efficient Byzantine Agreement
Micali, S. (2017) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itcs.2017.6 · Cited by 10 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- J. Chen and S. Micali. Algorand. Technical report, 2017. URL http://arxiv.org/abs/1607.01341. · arXiv
- E. Kokoris-Kogias, P. Jovanovic, N. Gailly, I. Khoffi, L. Gasser, and B. Ford. Enhancing Bitcoin security and performance with strong consistency via collec- tive signing. In Proceedings of the 25th Usenix Security Symposium, pages 279-296, Austin, TX, Aug. 2016.
- R. Kotla, L. Alvisi, M. Dahlin, A. Clement, and E. L. Wong. Zyzzyva: Speculative Byzantine fault tolerance. ACM Transactions on Computer Systems, 27(4):7:1-39, 2009.
- S. Micali. Fast and furious Byzantine agreement. In Proceedings of the Innovations in Theoretical Computer Science (ITCS) Conference, 2017.
- S. Micali, M. O. Rabin, and S. P. Vadhan. Verifiable ran- dom functions. In Proceedings of the 40th Annual IEEE Symposium on Foundations of Computer Science (FOCS), New York, NY, Oct. 1999.
Cited by
Sorted by citations
Blockchain for internet of things: a survey
Dai, H.-N., Zheng, Z., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2920987 · Cited by 1,246 · see citing works
Blockchain for AI: review and open research challenges
Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019) · IEEE Access · doi:10.1109/access.2018.2890507 · Cited by 1,001 · see citing works
Solutions to scalability of blockchain: a survey
Zhou, Q., Huang, H., Zheng, Z., & Bian, J. (2020) · IEEE Access · doi:10.1109/access.2020.2967218 · Cited by 689 · see citing works
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · see citing works
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
Blockchain's adoption in iot: the challenges, and a way forward
Makhdoom, I., Abolhasan, M., Abbas, H., & Ni, W. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.10.019 · Cited by 501 · see citing works
A Comprehensive Review of Blockchain Consensus Mechanisms
Lashkari, B., & Musilek, P. (2021) · IEEE Access · doi:10.1109/access.2021.3065880 · Cited by 499 · see citing works
Mitigating Sybils in Federated Learning Poisoning
Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385 · see citing works
Biscotti: A blockchain system for private and secure federated learning
Shayan, M., Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3044223 · Cited by 354 · see citing works
AI and Blockchain: A Disruptive Integration
Dinh, T. N., & Thai, M. T. (2018) · Computer · doi:10.1109/mc.2018.3620971 · Cited by 317 · see citing works
Lu, H., Huang, K., Azimi, M., & Guo, L. (2019) · IEEE Access · doi:10.1109/access.2019.2907695 · Cited by 265 · see citing works
Blockchain envisioned uav networks: challenges, solutions, and comparisons
Mehta, P., Gupta, R., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.01.023 · Cited by 266 · see citing works
Survey: sharding in blockchains
Yu, G., Wang, X., Yu, K., Ni, W., Zhang, J. A., & Liu, R. P. (2020) · IEEE Access · doi:10.1109/access.2020.2965147 · Cited by 267 · see citing works
A Blockchain‐Based Federated Learning Method for Smart Healthcare
Chang, Y., Fang, C., & Sun, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4376418 · Cited by 119 · see citing works
Yuan, L., He, Q., Tan, S., Li, B., Yu, J., Chen, F., & Yang, Y. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3231296 · Cited by 33 · see citing works
The challenges of existence, status, and value for improving blockchain
Lin, F., & Qiang, M. (2019) · IEEE Access · doi:10.1109/access.2018.2888697 · Cited by 22 · see citing works
Tangle Ledger for Decentralized Learning
Schmid, R., Pfitzner, B., Beilharz, J., Arnrich, B., & Polze, A. (2020) · 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) · doi:10.1109/ipdpsw50202.2020.00144 · Cited by 17 · see citing works
- Authors
- Gilad, Y., Hemo, R., Micali, S., Vlachos, G., & Zeldovich, N.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Venue
- Proceedings of the 26th Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2017-10-14
- Pages
- 51-68
- OpenAlex
- W2763398287
- Cited by
- 1,499
- References
- 37
- Citations by year
- 2026: 94 · 2025: 165 · 2024: 183 · 2023: 183 · 2022: 166 · 2021: 217 · 2020: 210 · 2019: 190 · 2018: 85 · 2017: 6
- FWCI
- 103.15
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM SIGOPS Symposium on Operating Systems Principles (SOSP) (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- Funders
- National Science Foundation (CNS-1413920 and CNS-1414119)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- Algorand, cryptocurrency, blockchain consensus, network partitioning, fork, scalability, Byzantine fault tolerance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
