True random number generator

As explained in Real-World Cryptography

A true random number generator is a device or system that derives random values from physical nondeterministic phenomena.

A true random number generator derives values from unpredictable physical phenomena rather than predictable software events. Thermal noise is one example. The resulting randomness is valuable in cryptography, where low-entropy strings are predictable and high-entropy strings are harder to predict. section 8.1

A practical TRNG pipeline has several concerns:

• Gather physical noise from sensors or hardware aids; boot-time interrupts alone may be predictable or externally influenced. section 8.1
• Clean and combine the noise before cryptographic use, because individual bits may be biased or correlated. section 8.1
• Expect extraction to be slower than consuming already-generated random values, so randomness generation can become a bottleneck. section 8.1
• Mix independent entropy sources contributively when possible, so one source cannot force the final result. section 8.4

Thermal noise in a processor

Modern Intel CPUs can extract randomness from thermal noise and expose it through the RDRAND instruction. Operating systems commonly mix such hardware output with other entropy sources rather than relying on one source alone. section 8.4

A smart-card component

A smart card may include a CPU, memory, inputs and outputs, and a hardware random number generator. The TRNG supports the card’s contained hardware applications and security functions. section 13.2.2

Trace how physical noise becomes cleaned randomness suitable for cryptographic applications. section 8.1

How Real-World Cryptography covers it

How deep it goes

Explains entropy sources, bias, extraction, performance, and contributory mixing, but stops short of implementation details.

Examples

Uses thermal noise, Intel RDRAND, boot-time entropy failures, and smart-card hardware as concrete examples.

What it assumes

Introduces entropy and unpredictability from zero while assuming only basic awareness of cryptographic applications.

What you can do after

Evaluate physical randomness sources, extraction needs, bottlenecks, and risks from relying on a single source.

How it connects

Connects TRNGs with entropy, randomness extractors, operating systems, RDRAND, smart cards, and nonce generation.

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Also explained in

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  Hacking CryptographyA hardware random number generator, or true random number generator, samples nondeterministic physical phenomena to produce unpredictable random values.

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Related concepts

• pseudo-random number generatorkind of · A pseudorandom number generator is a deterministic computational process that uses an initial seed or internal state to produce a sequence with random-like statistical properties.
• cryptographically secure random number generatorused by · A cryptographically secure random number generator produces computationally unpredictable values while resisting prediction and recovery of its internal state by feasible adversaries.
• thermal noiseuses · Thermal noise is random electrical fluctuation caused by the thermally driven motion of charge carriers in a physical system.
• ring oscillatorused by · An electronic oscillator formed by connecting an odd number of inverting logic gates in a ring.
• entropy sourceuses · An entropy source is a physical, computational, or operating-system process that supplies unpredictable or uncertain input for generating random data.

This explainer was generated by Manning's knowledge graph from the text of Real-World Cryptography. The section links point to the parts of the book it draws on.