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In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
In cryptography, a pseudorandom function family, abbreviated PRF, is a collection of efficiently-computable functions which emulate a random oracle in the following way: no efficient algorithm can distinguish (with significant advantage) between a function chosen randomly from the PRF family and a random oracle (a function whose outputs are fixed completely at random).
We can think of a pseudorandom number generator (PRNG) as a function that transforms a series of bits known as the state into a new state and a random number. That is, given a PRNG function and an initial state s t a t e 0 {\displaystyle \mathrm {state} _{0}} , we can repeatedly use the PRNG to generate a sequence of states and random numbers.
While /dev/urandom is still intended as a pseudorandom number generator suitable for most cryptographic purposes, the authors of the corresponding man page note that, theoretically, there may exist an as-yet-unpublished attack on the algorithm used by /dev/urandom, and that users concerned about such an attack should use /dev/random instead. [7]
There are subtle differences and distinctions in the use of the terms "generator" and "iterator", which vary between authors and languages. [5] In Python, a generator is an iterator constructor: a function that returns an iterator. An example of a Python generator returning an iterator for the Fibonacci numbers using Python's yield statement ...
Then, let's create a string consisting of i bits drawn from uniform, concatenated with the last one of the given bits, followed by the created m−i−1 bits. The resulting output is either H i or H i+1 , since the i+1 bit is either drawn from uniform or from G l .
For example, consider an address book entry that represents a single person along with zero or more phone numbers and zero or more addresses. This could be modeled in an object-oriented implementation by a "Person object " with an attribute/field to hold each data item that the entry comprises: the person's name, a list of phone numbers, and a ...
Pseudorandom generators may also be used to construct symmetric key cryptosystems, where a large number of messages can be safely encrypted under the same key. Such a construction can be based on a pseudorandom function family, which generalizes the notion of a pseudorandom generator.