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Pseudorandom generator

Pseudorandom generator is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Pseudorandom generator rather than just read about it. In short: In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution. The random seed itself is typically a short binary string drawn from the uniform distribution.

Key takeaways

  • Pseudorandom generator belongs to biology; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Pseudorandom generator to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Pseudorandom generator from memory before moving on to harder problems.

Reference excerpt

In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution. The random seed itself is typically a short binary string drawn from the uniform distribution. Many different classes of statistical tests have been considered in the literature, among them the class of all Boolean circuits of a given size. It is not known whether good pseudorandom generators for this class exist, but it is known that their existence is in a certain sense equivalent to (unproven) circuit lower bounds in computational complexity theory. Hence the construction of pseudorandom generators for the class of Boolean circuits of a given size rests on currently unproven hardness assumptions.

Definition Let A = { A : { 0 , 1 } n → { 0 , 1 } ∗ } {\displaystyle {\mathcal {A}}=\{A:\{0,1\}^{n}\to \{0,1\}^{*}\}} be a class of functions. These functions are the statistical tests that the pseudorandom generator will try to fool, and they are usually algorithms. Sometimes the statistical tests are also called adversaries or distinguishers. The notation in the codomain of the functions is the Kleene star. A function G : { 0 , 1 } ℓ → { 0 , 1 } n {\displaystyle G:\{0,1\}^{\ell }\to \{0,1\}^{n}} with ℓ < n {\displaystyle \ell <n} is a pseudorandom generator against A {\displaystyle {\mathcal {A}}} with bias ε {\displaystyle \varepsilon } if, for every A {\displaystyle A} in A {\displaystyle {\mathcal {A}}} , the statistical distance between the distributions A ( G ( U ℓ ) ) {\displaystyle A(G(U_{\ell }))} and A ( U n ) {\displaystyle A(U_{n})} is at most ε {\displaystyle \varepsilon } , where U k {\displaystyle U_{k}} is the uniform distribution on { 0 , 1 } k {\displaystyle \{0,1\}^{k}} . The quantity ℓ {\displaystyle \ell } is called the seed length and the quantity n − ℓ {\displaystyle n-\ell } is called the stretch of the pseudorandom generator. A pseudorandom generator against a family of adversaries ( A n ) n ∈ N {\displaystyle ({\mathcal {A}}_{n})_{n\in \mathbb {N} }} with bias ε ( n ) {\displaystyle \varepsilon (n)} is a family of pseudorandom generators ( G n ) n ∈ N {\displaystyle (G_{n})_{n\in \mathbb {N} }} , where G n : { 0 , 1 } ℓ ( n ) → { 0 , 1 } n {\displaystyle G_{n}:\{0,1\}^{\ell (n)}\to \{0,1\}^{n}} is a pseudorandom generator against A n {\displaystyle {\mathcal {A}}_{n}} with bias ε ( n ) {\displaystyle \varepsilon (n)} and seed length ℓ ( n ) {\displaystyle \ell (n)} . In most applications, the family A {\displaystyle {\mathcal {A}}} represents some model of computation or some set of algorithms, and one is interested in designing a pseudorandom generator with small seed length and bias, and such that the output of the generator can be computed by the same sort of algorithm.

In cryptography

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Pseudorandom generator

Start with the simplest possible case. Write down what Pseudorandom generator claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Pseudorandom generator before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Pseudorandom generator ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Pseudorandom generator

In research
Pseudorandom generator appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Pseudorandom generator in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Pseudorandom generator is common in secondary-school and first-year university syllabi. It links to neighbouring topics Algorithmic information theory, Cryptography, Pseudorandomness, so understanding it makes those chapters shorter.
In everyday life
Look for Pseudorandom generator outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Pseudorandom generator in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Pseudorandom generator means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Pseudorandom generator out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Pseudorandom generator in simple terms?

In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and t…

Why does Pseudorandom generator matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Pseudorandom generator?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Pseudorandom generator.

Tags

  • Algorithmic information theory
  • Cryptography
  • Pseudorandomness

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