Randomness extractor
A randomness extractor is an algorithm that transforms one or more imperfectly random sources into output with stronger, more uniform randomness properties.
Hacking Cryptography
A randomness extractor is an algorithm that converts a weakly random source into output with stronger and more uniform randomness properties.
- How it is used
- Conditions TRNG raw output after health testing before it seeds a cryptographic CSPRNG.
- How deep it goes
- Explains extractor placement and bias removal, but does not specify an extraction algorithm or construction.
- Examples
- Uses ambient-temperature bias in a physical source and a diagrammed TRNG-to-CSPRNG pipeline.
- What you can do after
- Operate/configure an entropy pipeline that tests raw TRNG output, extracts bias, and seeds a CSPRNG.
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Real-World Cryptography
A randomness extractor is a procedure that combines and transforms imperfect random inputs into output with improved randomness properties.
- How it is used
- Prepares noisy physical or device-derived inputs for cryptographic applications.
- How deep it goes
- Explains imperfect inputs and a hash-and-XOR pattern, but stops short of detailed extractor algorithms or operating-system methods.
- Examples
- Uses thermal noise, biased bits, correlated successive bits, and hashing followed by XOR.
- What you can do after
- Apply it by gathering multiple noisy sources, hashing them, and XORing their digests before cryptographic use.
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Related concepts
- hash functionuses · A hash function deterministically maps input data to a value in a bounded output range, commonly for indexing, lookup, comparison, or integrity checking.
- biasaddresses · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.
- logical XORuses · Logical XOR is a binary operation that produces true when exactly one operand is true and false when both operands have the same truth value.
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