Secure and Efficient Asynchronous Broadcast Protocols

By Christian Cachin, Klaus Kursawe, Frank Petzold, Victor Shoup. Annual International Cryptology Conference, 2001

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/148bb0458ddd69f347ea7d01c1828ed5c2bb15ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wenjing Lou
2. Elaine Shi
3. Ari Juels
4. Dan Boneh
5. Dusist Niyato
6. Dawn Song
7. Benny Pinkas
8. Nickolai Zeldovich

In Computer Networks and Communications

1. Elaine Shi
2. Dusist Niyato
3. Dinh Thai Hoang
4. Rafael Pass
5. Zehui Xiong
6. Dawn Song
7. Wenjing Lou
8. Dong In Kim

In Materials Chemistry

1. Dawn Song
2. Elaine Shi
3. Rafael Pass

Citations in the corpus

• Stadium: A Distributed Metadata-Private Messaging System (2017, methodology): “To establish a random seed in a secure manner, despite malicious servers, we use the commitment-based protocol as outlined in [7].”
• Hybrid Consensus: Efficient Consensus in the Permissionless Model (2016, methodology): “Since classical permissioned consensus [15, 19, 21, 25, 39, 40,42,45] has been studied and optimized for decades and have been shown to achieve responsiveness against 1/3 corruptions, our idea is to use a slow blockchain protocol (called snailchain) such as Nakamoto consensus [30, 46] to bootstrap f”
• All You Need is DAG (2021, methodology): “To address the fairness issues that naturally arise in interorganizational deployments, we focus on the classic long-lived Byzantine Atomic Broadcast (BAB) problem [13, 19], which in addition to total order, progress, and validity, guarantees eventual fairness.”
• From blockchain consensus back to Byzantine consensus (2017, unknown): “Since then, randomisation has been extensively used to help solving consensus [11, 32, 30].”
• A Survey of Distributed Consensus Protocols for Blockchain Networks (2019, background): “[58] as the benchmark, HoneyBadgerBFT actually follows Ben-Or’s ACS construction [56] for better communication efficiency.”
• SBFT: A Scalable and Decentralized Trust Infrastructure (2018, methodology): “By using threshold signatures [19], [20], [68], [73] one can reduce the outgoing collector message size from linear to constant.”
• A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018, background): “According to [14], [51], a blockchain updating protocol is said to achieve the (probabilistic) consensus (a.”
• The Honey Badger of BFT Protocols (2016, background): “[15], but we prefer this more descriptive phrase.”
• Scalable Bias-Resistant Distributed Randomness (2017, background): “[29] by adopting a point-of-no-return strategy implemented through the concept of a barrier, a specific point in the protocol execution after which the protocol always completes successfully, and by extending it to the asynchronous setting, where the adversary can break timing assumptions [14], [15]”
• Algorand: Scaling Byzantine Agreements for Cryptocurrencies (2017, background): “The value of seed0 specified in the genesis block is decided using distributed random number generation [14], after the public keys and weights for the initial set of participants are publicly known.”
• Dining Cryptographers Revisited (2004, background): “(See [7] for recent results in this area.”