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Mask generation function

Mask generation function is a mathematics 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 Mask generation function rather than just read about it. In short: A mask generation function (MGF) is a cryptographic primitive similar to a cryptographic hash function except that while a hash function's output has a fixed size, a MGF supports output of a variable length. In this respect, a MGF can be viewed as a extendable-output function (XOF): it can accept input of any length and process it to produce output of any length.

Key takeaways

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

Reference excerpt

A mask generation function (MGF) is a cryptographic primitive similar to a cryptographic hash function except that while a hash function's output has a fixed size, a MGF supports output of a variable length. In this respect, a MGF can be viewed as a extendable-output function (XOF): it can accept input of any length and process it to produce output of any length. Mask generation functions are completely deterministic: for any given input and any desired output length the output is always the same.

Definition

A mask generation function takes an octet string of variable length and a desired output length as input, and outputs an octet string of the desired length. There may be restrictions on the length of the input and output octet strings, but such bounds are generally very large. Mask generation functions are deterministic; the octet string output is completely determined by the input octet string. The output of a mask generation function should be pseudorandom, that is, if the seed to the function is unknown, it should be infeasible to distinguish the output from a truly random string.

Applications Mask generation functions, as generalizations of hash functions, are useful wherever hash functions are. However, use of a MGF is desirable in cases where a fixed-size hash would be inadequate. Examples include generating padding, producing one-time pads or keystreams in symmetric-key encryption, and yielding outputs for pseudorandom number generators.

Padding schemes Mask generation functions were first proposed as part of the specification for padding in the RSA-OAEP algorithm. The OAEP algorithm required a cryptographic hash function that could generate an output equal in size to a "data block" whose length was proportional to arbitrarily sized input message.

Random number generators NIST Special Publication 800-90A defines a class of cryptographically secure random number generators, one of which is the "Hash DRBG", which uses a hash function with a counter to produce a requested sequence of random bits equal in size to the requested number of random bits.

Examples Perhaps the most common and straightforward mechanism to build a MGF is to iteratively apply a hash function together with an incrementing counter value. The counter may be incremented indefinitely to yield new output blocks until a sufficient amount of output is collected. This is the approach used in MGF1.

MGF1 MGF1 is a mask generation function defined in the Public Key Cryptography Standard #1 published by RSA Laboratories. It is approved by the US National Institute of Standards and Technology for use in cryptography modules which meet Federal Information Processing Standards.

Options

H a s h {\displaystyle {\mathsf {Hash}}}

hash function ( h L e n {\displaystyle {\mathsf {hLen}}} denotes the length in octets of the hash function output)

Input

Z {\displaystyle Z}

seed from which mask is generated, an octet string

l {\displaystyle l}

intended length in octets of the mask, at most 2 32 ( h L e n ) {\displaystyle 2^{32}({\mathsf {hLen}})}

Output

m a s k {\displaystyle {\mathsf {mask}}}

mask, an octet string of length l {\displaystyle l} ; or "mask too long"

Steps

Example code Below is Python code implementing MGF1:

Example outputs of MGF1:

References

Worked examples

Example 1 — a first encounter with Mask generation function

Start with the simplest possible case. Write down what Mask generation function claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Mask generation function 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 Mask generation function 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 Mask generation function

In research
Mask generation function appears in mathematics 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 Mask generation function 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
Mask generation function is common in secondary-school and first-year university syllabi. It links to neighbouring topics Cryptographic hash functions, Cryptographic primitives, Theory of cryptography, so understanding it makes those chapters shorter.
In everyday life
Look for Mask generation function 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 Mask generation function in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Mask generation function 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 Mask generation function out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Mask generation function in simple terms?

A mask generation function (MGF) is a cryptographic primitive similar to a cryptographic hash function except that while a hash function's output has a fixed size, a MGF supports output of a variable length. In this respect, a MGF can be viewed as a extendable-output function (XOF): it can accept i…

Why does Mask generation function matter?

Because it connects several mathematics 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 Mask generation function?

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 Mask generation function.

Tags

  • Cryptographic hash functions
  • Cryptographic primitives
  • Theory of cryptography

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