Scalable Bias-Resistant Distributed Randomness

By Ewa Syta, Philipp Jovanovic, Eleftherios Kokoris-Kogias, Nicolas Gailly, Linus Gasser, Ismail Khoffi, Michael J. Fischer, Bryan Ford. 2017

334 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, methodology): “We consider a random partitioning strategy in which each user is assigned to one of the available groups uniformly at random, by using a bias-resistant public randomness generation protocol such as in [45].”
Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “This particular building block has appeared in several earlier systems, including Algorand [37] and Rand-Hound/RandHerd [71]; here, we adapt the approach from Algorand because, unlike RandHound/RandHerd, it can scale to millions of participants.”
A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks (2018, methodology): “By enabling parallelization of both data storage and network consensus, protocols aiming at ‘‘full sharding’’ are proposed in [175] and [176].”
OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding (2018, methodology): “To ensure that this sampling of representative validators is both scalable and strongly bias-resistant, OmniLedger uses RandHound [44], a protocol that serves this purpose under standard t-of-n threshold assumptions.”
Solutions to Scalability of Blockchain: A Survey (2020, methodology): “It uses a bias-resistant public-randomness protocol for shard assignment, which combines RandHound [82] with Algorand [16].”
Monoxide: Scale out Blockchains with Asynchronous Consensus Zones (2019, methodology): “Existing PoS systems adopt different methods to produce the randomness in the leader election to ensure decentralization and security [22, 7, 45, 18].”
Towards Scaling Blockchain Systems via Sharding (2018, methodology): “less identity blockchain. At the beginning, one node is selected at random to serve as a coordinator, using a veriable random function (VRF) [46]. Next, the coordinator drives the RandHound protocol [58] that collects inputs from the nodes and generates a fresh, non-bias random value. Figure 6 highl”
Verifiable Delay Functions (2018, result): “This stands in contrast to coin-tossing beacons which use verifiable secret sharing and are at best resistant to an adversary who controls a minority of the nodes [1, 67, 23].”
SBFT: A Scalable and Decentralized Trust Infrastructure (2018, methodology): “Several recent systems mention they plan to use BLS threshold signatures [46], [59], [76], [77].”
Chainspace: A Sharded Smart Contracts Platform (2017, methodology): “The node-toshard assignment is done every epoch using a bias-resistant decentralized randomness protocol [31] to prevent an adversary from compromising individual shards.”
SoK: Consensus in the Age of Blockchains (2017, background): “distributed way is a known hard problem: current best solutions tolerate up to 1/6 fraction of byzantine peers, while incurringa high message complexity[20]. Among the more recent solutions, RandHerd [128] providesa scalable, secure multi-partycomputationprotocolthat offers unbiasable, decentralized”