Distributed Random Number Generation is a method in the field of random number generation that enables multiple independent or semi-trusted parties to collaboratively produce a shared random number without relying on a central authority, ensuring fairness, verifiability, and resistance to manipulation by any subset of participants.
27
Publications
1.1K
Citations
67
Authors
36
Institutions
Publications per year
2017–2026
7
Parents
Publications
27
Scalable Bias-Resistant Distributed Randomness
Ewa Syta, Philipp Jovanovic, Eleftherios Kokoris Kogias et al. · 2017 · 273 citations · Full text
Proceedings of the 1989 winter simulation conference
Philip Heidelberger, Lee W. Schruben, Enver Yücesan et al. · 1989 · 78 citations
1–5 of 27
Authors
67
- HD
2
h-index
- LL
1
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- BF
1
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- JA
1
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- UD
1
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1–5 of 67
Institutions
36
University of Illinois Chicago
Chicago, United States
2 h-index
2 publications
57 citations
University of Florida
Gainesville, United States
1 h-index
1 publications
24 citations
Medical University of South Carolina
Charleston, United States
1 h-index
2 publications
34 citations
Technical University of Munich
Munich, Germany
1 h-index
1 publications
27 citations
University of Bonn
Bonn, Germany
1 h-index
1 publications
273 citations
NEC (Japan)
Tokyo, Japan
1 h-index
1 publications
53 citations
University of Salford
Salford, United Kingdom
1 h-index
1 publications
16 citations
University of Manitoba
Winnipeg, Canada
1 h-index
3 publications
42 citations
1–8 of 36
Venues
- Communication in Statistics- Theory and Methods
3 publications · 50 citations
3
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- Annals of the Institute of Statistical Mathematics
1 publications · 16 citations
1
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- Journal of Biopharmaceutical Statistics
JBS · 1 publications · 45 citations
1
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- SIAM Review
1 publications · 115 citations
1
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- Journal of Mathematical Sciences
1 publications · 56 citations
1
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