OptRand: Optimistically Responsive Reconfigurable Distributed Randomness
Adithya Bhat, Nibesh Shrestha, Aniket Kate, Kartik Nayak
Abstract
—Public random beacons publish random numbers at regular intervals, which anyone can obtain and verify. The design of public distributed random beacons has been an exciting research direction with significant implications for blockchains, voting, and beyond. Distributed random beacons, in addition to being bias-resistant and unpredictable, also need to have low communication overhead and latency, high resilience to faults, and ease of reconfigurability. Existing synchronous random beacon protocols sacrifice one or more of these properties. In this work, we design an efficient unpredictable synchronous random beacon protocol, OptRand, with quadratic (in the number n of system nodes) communication complexity per beacon output. First, we innovate by employing a novel combination of bilinear pairing based publicly verifiable secret-sharing and non-interactive zero-knowledge proofs to build a linear (in n ) sized publicly verifiable random sharing. Second, we develop a state machine replication protocol with linear-sized inputs that is also optimistically responsive, i.e., it can progress responsively at actual network speed during optimistic conditions, despite the synchrony assumption, and thus incur low latency. In addition, we present an efficient reconfiguration mechanism for OptRand that allows nodes to leave and join the system. Our experiments show our protocols perform significantly better compared to state-of-the-art protocols under optimistic conditions and on par with state-of-the-art protocols in the normal case. We are also the first to implement a reconfiguration mechanism for distributed beacons and demonstrate that our protocol continues to be live during reconfigurations.
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Install the CLIlune papers fulltext 67a82d6f-ebb8-4d64-b344-eb185a5db460Cited by top-tier papers3
- Random Beacons in Monte Carlo: Efficient Asynchronous Random Beacon without Threshold CryptographyAkhil Bandarupalli, Adithya Bhat, Saurabh Bagchi, Aniket Kate et al.CCS 2024 · 6 citations
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- SoK: Dlog-Based Distributed Key GenerationRenas Bacho, Alireza KavousiS&P 2025
Builds on8
- Sonic: Zero-Knowledge SNARKs from Linear-Size Universal and Updatable Structured Reference StringsMary Maller, Sean Bowe, Markulf Kohlweiss, Sarah MeiklejohnCCS 2019 · 412 citations
- Scalable Bias-Resistant Distributed RandomnessEwa Syta, Philipp Jovanovic, Eleftherios Kokoris-Kogias, Nicolas Gailly et al.S&P 2017 · 327 citations
- Sync HotStuff: Simple and Practical Synchronous State Machine ReplicationIttai Abraham, Dahlia Malkhi, Kartik Nayak, Ling Ren et al.S&P 2020 · 240 citations
- Spurt: Scalable Distributed Randomness Beacon with Transparent SetupSourav Das, Vinith Krishnan, Irene Miriam Isaac, Ling RenS&P 2022 · 80 citations
- Aggregatable Distributed Key GenerationKobi Gurkan, Philipp Jovanovic, Mary Maller, Sarah Meiklejohn et al.EUROCRYPT 2021 · 62 citations
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