Everything about Randomness extractor
Computational concept
A randomness extractor, often simply called an "extractor", is a function, which being applied to output from a weak entropy source, together with a short, uniformly random seed, generates a highly random output that appears independent from the source and uniformly distributed. Examples of weakly random sources include radioactive decay or thermal noise; the only restriction on possible sources is that there is no way they can be fully controlled, calculated or predicted, and that a lower bound on their entropy rate can be established. For a given source, a randomness extractor can even be considered to be a true random number generator (TRNG); but there is no single extractor that has been proven to produce truly random output from any type of weakly random source. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Randomness extractorA 5-minute orientation
- Pick up a classic book: Automate the Boring Stuff with PythonThe most-reprinted book on the subject
- Read the landmark paper: Postprocessing for quantum random-number generators: Entropy evaluation and randomness extractionCited 220 times
- Go sideways: Fuzzy extractorA closely related topic
- Go sideways: Randomness mergerA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
📚 Books worth reading
Automate the Boring Stuff with PythonAl Sweigart · 2015
Zero To Production In RustLuca Palmieri · 2022
System modelling and optimizationIFIP TC7 Conference on System Modelling and Optimization (17th 1995 Prague, Czech Republic) · 1996
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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