Verifiable Random Function
E413706
A Verifiable Random Function (VRF) is a cryptographic primitive that produces pseudo-random outputs along with proofs that anyone can verify to confirm the outputs were correctly generated from a given input and secret key.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Verifiable Random Function canonical | 1 |
| Verifiable Random Functions (FOCS 1999) | 1 |
| Verifiable Random Functions (VRFs) | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4087633 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verifiable Random Function Context triple: [Algorand blockchain protocol, uses, Verifiable Random Function]
-
A.
Blum–Blum–Shub pseudorandom number generator
The Blum–Blum–Shub pseudorandom number generator is a cryptographically secure generator based on the hardness of factoring large composite numbers, widely studied in theoretical computer science and cryptography.
-
B.
Blum–Micali pseudorandom number generator
The Blum–Micali pseudorandom number generator is a foundational cryptographic algorithm that produces provably secure pseudorandom bits based on number-theoretic hardness assumptions.
-
C.
Merkle–Damgård construction
The Merkle–Damgård construction is a fundamental method for building collision-resistant cryptographic hash functions from fixed-size compression functions, used in many classic hash algorithms like MD5 and SHA-1.
-
D.
Merkle puzzles
Merkle puzzles are an early cryptographic protocol that introduced the concept of public-key exchange by allowing two parties to establish a shared secret over an insecure channel using computationally asymmetric “puzzle” problems.
-
E.
Modern Cryptography, Probabilistic Proofs and Pseudorandomness
"Modern Cryptography, Probabilistic Proofs and Pseudorandomness" is a foundational textbook that systematically develops the theoretical underpinnings of modern cryptography, focusing on probabilistic proof techniques and the theory of pseudorandomness.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Verifiable Random Function Target entity description: A Verifiable Random Function (VRF) is a cryptographic primitive that produces pseudo-random outputs along with proofs that anyone can verify to confirm the outputs were correctly generated from a given input and secret key.
-
A.
Blum–Blum–Shub pseudorandom number generator
The Blum–Blum–Shub pseudorandom number generator is a cryptographically secure generator based on the hardness of factoring large composite numbers, widely studied in theoretical computer science and cryptography.
-
B.
Blum–Micali pseudorandom number generator
The Blum–Micali pseudorandom number generator is a foundational cryptographic algorithm that produces provably secure pseudorandom bits based on number-theoretic hardness assumptions.
-
C.
Merkle–Damgård construction
The Merkle–Damgård construction is a fundamental method for building collision-resistant cryptographic hash functions from fixed-size compression functions, used in many classic hash algorithms like MD5 and SHA-1.
-
D.
Merkle puzzles
Merkle puzzles are an early cryptographic protocol that introduced the concept of public-key exchange by allowing two parties to establish a shared secret over an insecure channel using computationally asymmetric “puzzle” problems.
-
E.
Modern Cryptography, Probabilistic Proofs and Pseudorandomness
"Modern Cryptography, Probabilistic Proofs and Pseudorandomness" is a foundational textbook that systematically develops the theoretical underpinnings of modern cryptography, focusing on probabilistic proof techniques and the theory of pseudorandomness.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
cryptographic primitive
ⓘ
public-key primitive ⓘ |
| canBeBasedOn |
RSA assumptions
ⓘ
bilinear pairings ⓘ discrete logarithm assumptions ⓘ elliptic curve assumptions ⓘ |
| distinguishingFeature |
anyone can verify correctness without learning secret key
ⓘ
binds randomness to specific input and key ⓘ |
| evaluationUses | secret key ⓘ |
| generalizes | pseudorandom function with public verifiability ⓘ |
| goal | produce random-looking outputs with public verifiability ⓘ |
| hasAbbreviation | VRF ⓘ |
| hasComponentAlgorithm |
evaluation algorithm
ⓘ
key generation algorithm ⓘ verification algorithm ⓘ |
| hasInput |
public input value
ⓘ
secret key ⓘ |
| hasOutput |
proof of correctness
ⓘ
pseudo-random value ⓘ |
| hasProperty |
collision resistance
ⓘ
completeness of proofs ⓘ deterministic given key and input ⓘ non-interactive verification ⓘ pseudo-random output ⓘ pseudorandomness under secret key ⓘ publicly verifiable proof ⓘ soundness of proofs ⓘ unique output per key-input pair ⓘ uniqueness of valid output ⓘ unpredictability ⓘ |
| introducedBy |
Michael Rabin
ⓘ
Salil Vadhan ⓘ Silvio Micali ⓘ |
| introducedIn | 1999 ⓘ |
| introducedInWork |
Verifiable Random Function
self-linksurface differs
ⓘ
surface form:
Verifiable Random Functions (FOCS 1999)
|
| relatedTo |
digital signature scheme
ⓘ
pseudorandom function ⓘ zero-knowledge proof ⓘ |
| requiresKeyType |
public key
ⓘ
secret key ⓘ |
| securityBasedOn | hardness of underlying public-key problem ⓘ |
| usedIn |
blockchain consensus protocols
ⓘ
committee selection in proof-of-stake systems ⓘ leader election in distributed systems ⓘ lotteries and random selection ⓘ privacy-preserving protocols ⓘ public randomness beacons ⓘ secure load balancing ⓘ |
| verificationUses | public key ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Verifiable Random Function Description of subject: A Verifiable Random Function (VRF) is a cryptographic primitive that produces pseudo-random outputs along with proofs that anyone can verify to confirm the outputs were correctly generated from a given input and secret key.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Verifiable Random Functions (VRFs)
Verifiable Random Function
→
introducedInWork
→
Verifiable Random Function
self-linksurface differs
ⓘ
this entity surface form:
Verifiable Random Functions (FOCS 1999)