---
_id: '8306'
abstract:
- lang: eng
  text: Bias-resistant public randomness is a critical component in many (distributed)
    protocols. Generating public randomness is hard, however, because active adversaries
    may behave dishonestly to bias public random choices toward their advantage. Existing
    solutions do not scale to hundreds or thousands of participants, as is needed
    in many decentralized systems. We propose two large-scale distributed protocols,
    RandHound and RandHerd, which provide publicly-verifiable, unpredictable, and
    unbiasable randomness against Byzantine adversaries. RandHound relies on an untrusted
    client to divide a set of randomness servers into groups for scalability, and
    it depends on the pigeonhole principle to ensure output integrity, even for non-random,
    adversarial group choices. RandHerd implements an efficient, decentralized randomness
    beacon. RandHerd is structurally similar to a BFT protocol, but uses RandHound
    in a one-time setup to arrange participants into verifiably unbiased random secret-sharing
    groups, which then repeatedly produce random output at predefined intervals. Our
    prototype demonstrates that RandHound and RandHerd achieve good performance across
    hundreds of participants while retaining a low failure probability by properly
    selecting protocol parameters, such as a group size and secret-sharing threshold.
    For example, when sharding 512 nodes into groups of 32, our experiments show that
    RandHound can produce fresh random output after 240 seconds. RandHerd, after a
    setup phase of 260 seconds, is able to generate fresh random output in intervals
    of approximately 6 seconds. For this configuration, both protocols operate at
    a failure probability of at most 0.08% against a Byzantine adversary.
article_processing_charge: No
author:
- first_name: E.
  full_name: Syta, E.
  last_name: Syta
- first_name: P.
  full_name: Jovanovic, P.
  last_name: Jovanovic
- first_name: Eleftherios
  full_name: Kokoris Kogias, Eleftherios
  id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
  last_name: Kokoris Kogias
- first_name: N.
  full_name: Gailly, N.
  last_name: Gailly
- first_name: L.
  full_name: Gasser, L.
  last_name: Gasser
- first_name: I.
  full_name: Khoffi, I.
  last_name: Khoffi
- first_name: M. J.
  full_name: Fischer, M. J.
  last_name: Fischer
- first_name: B.
  full_name: Ford, B.
  last_name: Ford
citation:
  ama: 'Syta E, Jovanovic P, Kokoris Kogias E, et al. Scalable bias-resistant distributed
    randomness. In: <i>2017 IEEE Symposium on Security and Privacy</i>. IEEE; 2017:444-460.
    doi:<a href="https://doi.org/10.1109/SP.2017.45">10.1109/SP.2017.45</a>'
  apa: 'Syta, E., Jovanovic, P., Kokoris Kogias, E., Gailly, N., Gasser, L., Khoffi,
    I., … Ford, B. (2017). Scalable bias-resistant distributed randomness. In <i>2017
    IEEE Symposium on Security and Privacy</i> (pp. 444–460). San Jose, CA, United
    States: IEEE. <a href="https://doi.org/10.1109/SP.2017.45">https://doi.org/10.1109/SP.2017.45</a>'
  chicago: Syta, E., P. Jovanovic, Eleftherios Kokoris Kogias, N. Gailly, L. Gasser,
    I. Khoffi, M. J. Fischer, and B. Ford. “Scalable Bias-Resistant Distributed Randomness.”
    In <i>2017 IEEE Symposium on Security and Privacy</i>, 444–60. IEEE, 2017. <a
    href="https://doi.org/10.1109/SP.2017.45">https://doi.org/10.1109/SP.2017.45</a>.
  ieee: E. Syta <i>et al.</i>, “Scalable bias-resistant distributed randomness,” in
    <i>2017 IEEE Symposium on Security and Privacy</i>, San Jose, CA, United States,
    2017, pp. 444–460.
  ista: 'Syta E, Jovanovic P, Kokoris Kogias E, Gailly N, Gasser L, Khoffi I, Fischer
    MJ, Ford B. 2017. Scalable bias-resistant distributed randomness. 2017 IEEE Symposium
    on Security and Privacy. SP: Symposium on Security and Privacy, 444–460.'
  mla: Syta, E., et al. “Scalable Bias-Resistant Distributed Randomness.” <i>2017
    IEEE Symposium on Security and Privacy</i>, IEEE, 2017, pp. 444–60, doi:<a href="https://doi.org/10.1109/SP.2017.45">10.1109/SP.2017.45</a>.
  short: E. Syta, P. Jovanovic, E. Kokoris Kogias, N. Gailly, L. Gasser, I. Khoffi,
    M.J. Fischer, B. Ford, in:, 2017 IEEE Symposium on Security and Privacy, IEEE,
    2017, pp. 444–460.
conference:
  end_date: 2017-05-26
  location: San Jose, CA, United States
  name: 'SP: Symposium on Security and Privacy'
  start_date: 2017-05-22
date_created: 2020-08-26T12:26:08Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2021-01-12T08:18:02Z
day: '01'
doi: 10.1109/SP.2017.45
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://eprint.iacr.org/2016/1067
month: '06'
oa: 1
oa_version: Preprint
page: 444-460
publication: 2017 IEEE Symposium on Security and Privacy
publication_identifier:
  isbn:
  - '9781509055340'
  issn:
  - 2375-1207
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Scalable bias-resistant distributed randomness
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2017'
...