Byzantine fault tolerance
E471415
Byzantine fault tolerance is a property of distributed systems that enables them to continue operating correctly even when some components behave arbitrarily or maliciously.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Byzantine agreement protocols | 1 |
| Byzantine fault tolerance canonical | 1 |
| Byzantine fault tolerance theory | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4765356 — 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: Byzantine fault tolerance Context triple: [Byzantine Generals Problem, relatedConcept, Byzantine fault tolerance]
-
A.
Practical Byzantine Fault Tolerance
Practical Byzantine Fault Tolerance is a consensus algorithm for distributed systems that efficiently tolerates Byzantine (arbitrary) faults, enabling reliable operation even when some nodes behave maliciously or unpredictably.
-
B.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
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C.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
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D.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
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E.
FLP impossibility result
The FLP impossibility result is a foundational theorem in distributed computing showing that in an asynchronous system, no deterministic consensus protocol can guarantee both safety and liveness in the presence of even a single crash failure.
- 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: Byzantine fault tolerance Target entity description: Byzantine fault tolerance is a property of distributed systems that enables them to continue operating correctly even when some components behave arbitrarily or maliciously.
-
A.
Practical Byzantine Fault Tolerance
Practical Byzantine Fault Tolerance is a consensus algorithm for distributed systems that efficiently tolerates Byzantine (arbitrary) faults, enabling reliable operation even when some nodes behave maliciously or unpredictably.
-
B.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
-
C.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
-
D.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
-
E.
FLP impossibility result
The FLP impossibility result is a foundational theorem in distributed computing showing that in an asynchronous system, no deterministic consensus protocol can guarantee both safety and liveness in the presence of even a single crash failure.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
consensus property
ⓘ
distributed computing concept ⓘ fault tolerance model ⓘ |
| addresses |
Byzantine faults
ⓘ
arbitrary node failures ⓘ inconsistent or conflicting messages from faulty nodes ⓘ malicious node behavior ⓘ |
| alsoKnownAs | BFT ⓘ |
| appliesTo |
blockchain systems
ⓘ
distributed databases ⓘ mission-critical distributed systems ⓘ replicated services ⓘ |
| assumes |
bounded number of Byzantine nodes
ⓘ
reliable cryptographic primitives in many protocols ⓘ |
| constrains | maximum fraction of faulty nodes that can be tolerated ⓘ |
| contrastsWith | crash fault tolerance ⓘ |
| definedAs | ability of a distributed system to function correctly despite arbitrary faults ⓘ |
| enables |
operation in presence of compromised nodes
ⓘ
robustness against insider attacks in distributed systems ⓘ |
| formalizedIn | Byzantine Generals Problem ⓘ |
| goal |
maintain correct overall system behavior
ⓘ
prevent faulty nodes from breaking safety ⓘ reach agreement among honest nodes ⓘ |
| hasProperty |
ensures liveness under certain network assumptions
ⓘ
ensures safety despite faulty nodes ⓘ tolerates arbitrary faults up to a threshold ⓘ |
| implies | system can mask arbitrary node misbehavior up to a limit ⓘ |
| influences |
design of consensus algorithms
ⓘ
security models for distributed ledgers ⓘ |
| oftenFormalizedAs |
Byzantine agreement problem
ⓘ
linked to:
Byzantine Generals Problem
|
| oftenRequires | at least 3f+1 replicas to tolerate f Byzantine faults in classical models ⓘ |
| relatedTo |
Byzantine Generals Problem
ⓘ
Byzantine agreement protocols ⓘ
linked to:
Byzantine fault tolerance
PBFT ⓘ
linked to:
Practical Byzantine Fault Tolerance
Practical Byzantine Fault Tolerance ⓘ blockchain consensus ⓘ distributed consensus ⓘ state machine replication ⓘ |
| requires |
agreement among non-faulty nodes
ⓘ
message authentication or integrity mechanisms ⓘ redundant nodes ⓘ |
| studiedIn |
cryptography and security
ⓘ
distributed systems research ⓘ fault-tolerant computing ⓘ |
| usedIn |
Byzantine fault-tolerant replicated state machines
ⓘ
critical control systems requiring high reliability ⓘ permissioned blockchain platforms ⓘ |
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: Byzantine fault tolerance Description of subject: Byzantine fault tolerance is a property of distributed systems that enables them to continue operating correctly even when some components behave arbitrarily or maliciously.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Byzantine fault tolerance
linked to: Byzantine fault tolerance