SCRAPE: Scalable randomness attested by public entities

Ignacio Cascudo, Bernardo David*

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

60 Citations (Scopus)

Abstract

Uniform randomness beacons whose output can be publicly attested to be unbiased are required in several cryptographic protocols. A common approach to building such beacons is having a number parties run a coin tossing protocol with guaranteed output delivery (so that adversaries cannot simply keep honest parties from obtaining randomness, consequently halting protocols that rely on it). However, current constructions face serious scalability issues due to high computational and communication overheads. We present a coin tossing protocol for an honest majority that allows for any entity to verify that an output was honestly generated by observing publicly available information (even after the execution is complete), while achieving both guaranteed output delivery and scalability. The main building block of our construction is the first Publicly Verifiable Secret Sharing scheme for threshold access structures that requires only O(n) exponentiations. Previous schemes required O(nt) exponentiations (where t is the threshold) from each of the parties involved, making them unfit for scalable distributed randomness generation, which requires t = n/2 and thus O(n2) exponentiations.

Original languageEnglish
Title of host publicationApplied Cryptography and Network Security : 15th International Conference, ACNS 2017, Proceedings
Number of pages20
PublisherSpringer
Publication date2017
Pages537-556
ISBN (Print)978-3-319-61203-4
ISBN (Electronic)978-3-319-61204-1
DOIs
Publication statusPublished - 2017
Event15th International Conference on Applied Cryptography and Network Security, ACNS 2017 - Kanazawa, Japan
Duration: 10 Jul 201712 Jul 2017

Conference

Conference15th International Conference on Applied Cryptography and Network Security, ACNS 2017
Country/TerritoryJapan
CityKanazawa
Period10/07/201712/07/2017
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10355 LNCS
ISSN0302-9743

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