On Distributed Randomness Generation in Blockchains

Ilan Komargodski*, Yoav Tamir

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present a smart contract for generating unbiased randomness on a blockchain/ledger-style system. Our smart contract can be stored on a blockchain and be executed publicly whenever needed. In particular, our protocol is suitable for leader-election-style applications and for mitigating front-running attacks. We prove correctness and security of our smart contract within a formal model of a distributed ledger extended by financial incentives and smart contracts. To the best of our knowledge, our formalization is the first to capture the concept of “time” in the context of smart contracts. Furthermore, we show that our protocol is incentive compatible, namely, under some reasonable financial assumption, following the honest execution, even for an all but one corruptions, gives the most financial benefit. Technically, our smart contract utilizes recently-introduced non-malleable time-lock puzzles. At a very high level, these are cryptographic commitments that can be “force opened” after a predefined delay. Using those, we implement a commit-and-reveal-style protocol but where we consider the particulars of the blockchain setting.

Original languageAmerican English
Title of host publicationCyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
EditorsShlomi Dolev, Ehud Gudes, Pascal Paillier
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031346705
StatePublished - 2023
Event7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023 - Be'er Sheva, Israel
Duration: 29 Jun 202330 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13914 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
CityBe'er Sheva

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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