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Commitment Schemes

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Hash Functions and Collision ResistanceOne-Way FunctionsSecure Multi-Party ComputationZero-Knowledge Proofs Advanced
commitment hiding binding pedersen hash-commitment

Core Idea

A commitment scheme lets a party commit to a value while keeping it hidden (hiding), with the guarantee that they cannot later change the committed value (binding). Think of sealing a value in an envelope: the envelope hides the value, and you cannot change it after sealing. Commitments have two phases: commit (produce a commitment c to value v) and reveal (open c to show v). Hash-based commitments (c = H(v || r) for random r) provide computational hiding and binding. Pedersen commitments (c = gv * hr) provide information-theoretic hiding with computational binding. Commitments are essential building blocks for zero-knowledge proofs, coin-flipping protocols, secure computation, and blockchain applications.

Explainer

A commitment scheme is one of the most fundamental primitives in cryptography, enabling a party to "lock in" a value while keeping it secret, and later reveal it with proof that they haven't changed it. The protocol has two phases: in the commit phase, the committer produces a commitment c to a value v (like sealing v in an envelope). In the reveal phase, the committer opens c by revealing v and any randomness used, and anyone can verify that c was indeed a commitment to v. Two security properties must hold: hiding (the commitment reveals nothing about v to an observer) and binding (the committer cannot open c to a different value v').

The simplest construction is hash-based: c = H(v || r) where r is a random nonce. Revealing means sending v and r; verification checks that H(v || r) = c. Hiding holds because the random r makes c computationally indistinguishable from a random hash output (even if v comes from a small set). Binding holds because finding v' != v and r' with H(v' || r') = H(v || r) requires a hash collision — computationally infeasible. The Pedersen commitment (c = gv * hr in a group where log_g(h) is unknown) achieves information-theoretic hiding: for any c and any target v', there exists an r' making c = gv' * hr', so the commitment is consistent with every possible value — no computation can extract v. Binding is computational: opening to two different values would reveal log_g(h).

A fundamental impossibility result constrains the design space: perfect hiding and perfect binding cannot coexist. If the commitment is consistent with every value (perfect hiding), the committer could in principle find an alternative opening (breaking perfect binding, though this requires solving a hard problem). If the commitment uniquely determines the value (perfect binding), an unbounded adversary could find that value (breaking perfect hiding). Every scheme must choose one property to be information-theoretic and the other computational.

Commitments are ubiquitous in cryptographic protocols. Zero-knowledge proofs use commitments to let the prover lock in their responses before seeing the verifier's challenges (preventing cheating). Fair coin-flipping (Blum's protocol) uses commitments so neither party can bias the outcome. Secure computation protocols use commitments to enforce honest behavior. Blockchain applications use commitments extensively: Merkle trees (vector commitments for block contents), Pedersen commitments (hiding transaction amounts in confidential transactions), and polynomial commitments (KZG, used in blockchain proof systems). The humble commitment — "I've decided, but I'm not telling you yet" — is one of the most versatile building blocks in the entire cryptographic toolkit.

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Prerequisite Chain

Understanding ZeroThe Number ZeroCounting to FiveCounting to 10Counting to 20Counting a Set of Objects Up to 20Cardinality: The Last Number CountedMatching Numerals to QuantitiesSubitizing Small QuantitiesAddition Within 10Number Bonds to 10Addition Within 20Doubles and Near DoublesDoubles Facts Within 10Near Doubles Facts Within 20Mental Math Strategies for AdditionMental Math: Adding and Subtracting TensAddition Within 100Repeated Addition as MultiplicationMultiplication as Equal GroupsMultiplication: ArraysBasic Multiplication Facts (0s, 1s, 2s, 5s, 10s)Multiplication Facts Within 100Division as Equal SharingDivision as Grouping (Measurement Division)Division: Grouping (Repeated Subtraction) ModelDivision: Fair Sharing ModelDivision as Equal SharingDivision as GroupingBasic Division FactsDivision Facts Within 100Multiplication and Division Fact FamiliesRelationship Between Multiplication and DivisionDivision Facts as Inverse of MultiplicationRemainders and Quotients in DivisionDivision Word ProblemsMulti-Step Word ProblemsSolving Multi-Step Word ProblemsMultiplication Word ProblemsDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueIntegers and the Number LineComparing and Ordering IntegersAbsolute ValueAdding IntegersSubtracting IntegersMultiplying IntegersIntroduction to ExponentsOrder of OperationsInteger Order of OperationsVariable ExpressionsThe Distributive PropertyVariables and Expressions ReviewIntroduction to PolynomialsAdding and Subtracting PolynomialsMultiplying PolynomialsFactorialPermutationsCombinationsCounting Principles: Addition and Multiplication RulesIntroduction to Graph TheoryPropositional Logic FoundationsLogical EquivalencesBoolean AlgebraBoolean Type and Truth ValuesComparison Operators and Boolean TestsLogical Operators and Boolean AlgebraBoolean Algebra and Fundamental LawsLogic Gates FundamentalsImplementing Boolean Functions with GatesKarnaugh Map SimplificationCombinational Circuit DesignFlip-Flops and LatchesFinite State Machines (FSMs)Deterministic Finite Automata (DFA)Nondeterministic Finite Automata (NFA)Two-Way Finite AutomataNFA to DFA Conversion (Subset Construction)DFA Properties and Minimization AlgorithmsRegular Languages: Definition and CharacterizationContext-Free Grammars (CFGs)Pushdown Automata (PDA)Equivalence of CFGs and Pushdown AutomataClosure Properties of Context-Free LanguagesLimitations of Context-Free LanguagesPumping Lemma for Context-Free LanguagesTuring MachinesVariants of Turing Machines and EquivalenceNondeterministic Time Complexity and NPThe P vs. NP ProblemComplexity Class P: Polynomial TimeHash Functions and Collision ResistanceThe RSA CryptosystemComputational Hardness AssumptionsOne-Way FunctionsCommitment Schemes

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