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HydRand: Efficient Continuous
Distributed Randomness
Philipp Schindler*
, Aljosha Judmayer*
, Nicholas Stifter*†
,
Edgar Weippl*†
IEEE S&P 2020
SBA Research, 2020
*
SBA Research, †
TU Wien
2
https://xkcd.com/221
3
4
Local vs. Distributed Randomness
Local
• os-builtins primitives,
e.g. /dev/urandom
• dedicated hardware devices
• (typically) kept secret
• individually used, e.g. seed
for cryptographic keys
Distributed
• built on-top of local primitives
• multi-party protocol
• secret first, but published at a
specific point in time
• collectively used
5
A Randomness Beacon
We propose a solution employing a “beacon” which emits at
regularly spaced time intervals, randomly chosen integers in
the range 1 ⩽ i ⩽ k. (Rabin, 1983)
6
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
7
Motivation & Applications
Selection Processes
• gambling & lotteries services, jury selection, sport draws
Blockchain & BFT Protocols
• leader & committee selection, sharding, Smart Contracts
(Online-)Gaming
• shuffling, distribution of in-game items, team/tournament
assignment
Cryptographic Protocols
• parameter generation, privacy preserving messaging,
anonymous browsing
8
Cryptographic Building Blocks
Digital Signatures
• Ed25519
Cryptographic Hash Functions
• SHA3-256
Public-Verifiable Secret Sharing (PVSS)
• Scrape's PVSS
9
Secret Sharing
Distribution Reconstruction
S1
S2
S3
S4
S5
S
S
S2
S4
S5
dealer
participants
subset of participants
10
(Publicly-Verifiable) Secret Sharing
Shamir’s Secret Sharing
• (t, n) threshold scheme
• dealer distributes secret value
s to n participants
• any set of at least t participants
can reconstruct s
• dealer must be trusted
PVSS
• (t, n) threshold scheme
• correctness of shares can be
verified prior to reconstruction
• uses non-interactive zero
knowledge proofs
• malicious dealers are
detected
11
System and Threat Model
Fixed set of known participants
• n nodes total, f may deviate arbitrarily from the protocol
• standard n = 3f + 1 assumption
• t = f + 1 for PVSS threshold
Network
• synchronous, known upper bound on network delay
• authenticated point-to-point messaging channels
No DKG
No common broadcast channel
12
High-Level View on HydRand
Setup
• exchange public keys & initial PVSS shares
• determine initial random beacon value
Execution
• propose phase
• acknowledge phase
• vote phase
⇒ new random beacon value
⇒ new leader
round
13
Propose Acknowledge Vote Propose...
leader
14
Propose Acknowledge Vote Propose...
leader
S1 S1
S3S4
shares for new secret
(encrypted)
S revealed secret
15
Propose Acknowledge Vote Propose...
16
Propose Acknowledge Vote Propose...
S revealed secret
H( ) sig( )
17
Propose Acknowledge Vote Propose...
18
confirm vote
Propose Acknowledge Vote Propose...
19
confirm vote
Propose Acknowledge Vote Propose...
S2
share for secret
(decrypted)
recover vote
20
confirm vote
Propose Acknowledge Vote Propose...
Compute beacon output
confirm vote
confirm vote
confirm vote
S revealed secret
S revealed secret
S revealed secret
revealed secretS
21
confirm vote
Propose Acknowledge Vote Propose...
S2
share for secret
(decrypted)
recover vote
Compute beacon output
confirm vote
confirm vote
confirm vote
S revealed secret
S revealed secret
S revealed secret
revealed secret
S2
share for secret
(decrypted)
recover vote
S5
share for secret
(decrypted)
recover vote
S2
S4
S5
recovered secret
S
S
22
confirm vote
Propose Acknowledge Vote Propose...
S2
share for secret
(decrypted)
recover vote
Compute beacon output
confirm vote
confirm vote
confirm vote
S revealed secret
S revealed secret
S revealed secret
revealed secret
S2
share for secret
(decrypted)
recover vote
S5
share for secret
(decrypted)
recover vote
S2
S4
S5
recovered secret
S
S
=
23
Propose Acknowledge Vote Propose...
Compute beacon output
S revealed secret
S revealed secret
S revealed secret
revealed secret
S2
S4
S5
recovered secret
S
S
=
HRprev Rnew
24
Propose Acknowledge Vote Propose...
Derive next leader
Rnew
Leader is derived via previous output
• non-interactively
• uniformly at random from the set of potential leaders
Potential leaders:
• were not recently selected as leader
• did fulfil their duties as leader so far
25
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
26
Security Analysis: Unpredictability
Guaranteed after f+1 rounds
Additional probabilistic guarantees
27
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
28
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
29
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
30
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
31
Scalability / Throughput
https://github.com/PhilippSchindler/hydrand
32
Evaluation Results
Verification
• duration: ~57ms
• proof size: ~26kB
Network bandwidth CPU Utilization
33
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
34
Desired Properties
?
Bias-Resistance
Scalability
Unpredictability Liveness
Public-Verifiability
Energy Efficiency
Guaranteed Output Delivery
35
Outlook
Scalability improvements
• threshold cryptography
• pre-sharing PVSS secrets
• multiple threads for verification
Rejoining / dynamic participation
Different model
• partially synchronous network
• n = 2f + 1 threshold model
36
37
Random numbers should not be generated with a method
chosen at random. (Donald Knuth)
38
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl
HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl

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HydRand: Efficient Continuous Distributed Randomness. IEEE S&P 2020 by Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, Edgar Weippl