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Pseudorandom Generators

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One-Way FunctionsRandom Number Generation in CryptographyPseudorandom Functions
prg computational-indistinguishability seed-expansion hard-core-bit

Core Idea

A pseudorandom generator (PRG) is a deterministic function G: {0,1}^n → {0,1}^l(n) with l(n) > n (expansion) such that the output on a random seed is computationally indistinguishable from a truly random string of length l(n). No efficient statistical test can tell PRG output from random with non-negligible advantage. PRGs exist if and only if one-way functions exist (HILL theorem). The Goldreich-Levin hard-core bit construction turns any OWF into a PRG by extracting one pseudorandom bit per invocation, then iterating. PRGs are the theoretical foundation of stream ciphers and the basis for building pseudorandom functions.

Explainer

A pseudorandom generator (PRG) is a deterministic algorithm that stretches a short, truly random seed into a longer output that is computationally indistinguishable from a truly random string of the same length. The seed might be 128 bits; the output might be megabytes. No efficient algorithm — no statistical test, no machine learning model, no adversarial strategy running in polynomial time — can tell the PRG's output from genuine randomness with non-negligible advantage. This is a strictly computational guarantee: an all-powerful adversary could detect the pseudorandomness (only 2128 of the 2huge possible output strings are reachable), but no adversary with bounded resources can.

The existence of PRGs is equivalent to the existence of one-way functions — the most fundamental assumption in cryptography. The forward direction (OWFs imply PRGs) was proven by Hastad, Impagliazzo, Levin, and Luby (the HILL theorem). The construction uses the Goldreich-Levin hard-core bit: given any one-way function f, there exists a predicate b(x) that is easy to compute from x but looks random given only f(x). Define G(x) = (f(x), b(x)) — this expands by one bit, and the extra bit is pseudorandom. Iterating (compute f, extract a hard-core bit, use f(x) as the new state) yields arbitrary expansion. The reverse direction is simpler: a PRG is itself a one-way function (inverting G on a random output requires finding the seed from an exponentially small set).

PRGs are the theoretical foundation of stream ciphers (which produce a long keystream from a short key and encrypt by XOR) and the first step in the construction chain that builds all of symmetric cryptography from OWFs: OWFs → PRGs → pseudorandom functions → MACs → secure encryption. Each step in this chain is proven by reduction, ensuring that breaking the higher-level primitive implies breaking the lower-level one, which ultimately implies breaking the one-way function.

In practice, deployed CSPRNGs (ChaCha20, AES-CTR used as a PRNG) are not built through the theoretical OWF-to-PRG construction — that construction is correct but horrendously slow, extracting one pseudorandom bit per OWF invocation. Instead, practical PRGs are designed directly using concrete ciphers and analyzed under specific assumptions about those ciphers. The theoretical framework provides the definitions (what it means to be pseudorandom) and the feasibility result (PRGs can exist), while practice provides efficient instantiations. Understanding the theory explains why the definitions look the way they do and what guarantees they provide — which is essential for correctly using and analyzing the practical constructions.

Practice Questions 5 questions

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 FunctionsPseudorandom Generators

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