Information-Theoretic Security

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perfect secrecy one-time pad Shannon secrecy wiretap channel security

Core Idea

Information-theoretic security provides secrecy guarantees that hold against adversaries with unlimited computational power, unlike computational security (which assumes hard problems remain hard). Shannon proved that perfect secrecy — I(M; C) = 0, where M is the message and C is the ciphertext — requires the key to be at least as long as the message (the one-time pad achieves this bound). Wyner's wiretap channel extends this to noisy channels: when the eavesdropper has a degraded channel compared to the legitimate receiver, positive secrecy rates are achievable without any shared key. Information-theoretic security is unconditional — it cannot be broken by future algorithmic advances, quantum computers, or increased computing power.

Explainer

Most modern cryptography is computationally secure: AES, RSA, and elliptic curve cryptography rely on the assumption that certain problems (factoring, discrete logarithm) are computationally hard. If someone proved P = NP or built a sufficiently powerful quantum computer, these systems would break. Information-theoretic security eliminates this risk entirely by proving that the ciphertext contains zero information about the message, regardless of the adversary's capabilities.

Shannon formalized this in 1949. Perfect secrecy means I(M; C) = 0: the ciphertext C is statistically independent of the message M. Observing C does not change the adversary's beliefs about M at all — not even by one bit. Shannon proved that this requires H(K) >= H(M): the key must have at least as much entropy as the message. The one-time pad achieves this bound: C = M XOR K, where K is a uniformly random key the same length as M. Each ciphertext is equally likely under any message, providing perfect secrecy. But the key can never be reused (reuse leaks information via C_1 XOR C_2 = M_1 XOR M_2), making key management the central challenge.

Wyner's wiretap channel (1975) showed that physical-layer noise can provide secrecy without any key. If the sender communicates over a noisy channel to a legitimate receiver, and an eavesdropper observes a degraded version, the sender can encode messages so that the eavesdropper learns nothing while the legitimate receiver decodes correctly. The secrecy capacity C_s is the difference between the main channel capacity and the eavesdropper's channel capacity. The coding scheme uses stochastic encoding: the sender adds deliberate randomness that creates confusion for the eavesdropper but can be resolved by the legitimate receiver.

The modern relevance of information-theoretic security is growing. Quantum key distribution (QKD) provides information-theoretically secure key exchange using quantum physics. Physical-layer security extends the wiretap channel to practical wireless scenarios (fading, MIMO, cooperative jamming). Secret sharing and secure multi-party computation use information-theoretic tools to distribute secrets and compute functions without revealing private inputs. As quantum computing threatens computational security assumptions, unconditional security guarantees become increasingly valuable for applications where long-term secrecy is required — government communications, medical records, financial data with decades-long sensitivity.

Practice Questions 3 questions

Prerequisite Chain

Counting to 10Counting to 20Understanding ZeroThe Number ZeroCounting to FiveOne-to-One CorrespondenceCombining Small Groups Within 5Addition Within 10Addition Within 20Two-Digit Addition Without RegroupingTwo-Digit Addition with RegroupingAddition Within 100Repeated Addition as MultiplicationMultiplication 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 100Two-Digit by One-Digit DivisionDivision with RemaindersRemainders and Quotients in DivisionDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueReading and Writing DecimalsComparing and Ordering DecimalsAdding and Subtracting DecimalsMultiplying DecimalsDividing DecimalsDividing FractionsMixed Number ArithmeticOrder of OperationsInteger Order of OperationsVariable ExpressionsCombining Like TermsOne-Step EquationsTwo-Step EquationsSolving Multi-Step EquationsEquations with Variables on Both SidesAngle Pairs: Complementary, Supplementary, and VerticalParallel Lines and TransversalsCorresponding AnglesAlternate Interior AnglesTriangle Angle Sum TheoremExterior Angle TheoremTriangle Inequality TheoremSimilar Triangles: AA SimilaritySimilar Triangles: SSS and SAS SimilarityProportions in Similar TrianglesRight Triangle Trigonometry IntroductionTrigonometric Ratios ReviewRadian MeasureConverting Between Degrees and RadiansThe Unit CircleGraphing Sine and CosineGraphing Tangent and Reciprocal Trigonometric FunctionsDerivatives of Trigonometric FunctionsAntiderivativesIndefinite IntegralsBasic Integration RulesRiemann SumsDefinite Integral DefinitionProbability Density Functions and Continuous DistributionsCumulative Distribution FunctionsContinuous Random VariablesProbability Density FunctionsShannon EntropyJoint and Conditional EntropyMutual InformationKL DivergenceInformation-Theoretic Security

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