Pseudorandom Functions

In order to understand what a pseudorandom function generator (PRFG) is, one needs to understand what it means for a function to be random or pseudorandom.

A truly random function $H : {0, 1}^{S} \to {0, 1}^{l_{out}}$ is a function chosen according to the uniform distribution of all functions that take a string of length $S$ and output a string of length ${0, 1}^{l_{out}}$ . Alternatively, a random function can be thought of as a function which outputs a random string of length $l_{out}$ for every input $i \in {0, 1}^{S}$ , called an input data block (IDB). This can be pictured as a table of all possible IDBs and their corresponding, at the beginning undetermined, outputs. Whenever $H$ is invoked with an IDB $i$ , that IDB is looked up in the table. If its entry already has an output, then this value is directly returned. Otherwise, the function $H$ "flips a coin" $l_{out}$ times to determine each bit of the output, fills the generated output in the table and finally returns it. Subsequent queries for the same input data block will provide the already generated output.

Note

The input to a PRF may sometimes be treated as an integer between $0$ and $2^{S} - 1$ , which can be represented as a binary string of length $S$ . In these cases, it is called an index instead of an input data block.

The reason that these two notions of a random function are equivalent is that each "coin toss" can be thought of as making a step forward in search for the function $H$ which on input a specific $i$ outputs a specific output $o$ . Before the first coin flip, there are $2^{l_{out}}$ possible outputs. After the first coin flip, there are $2^{l_{out} - 1}$ possible outputs - the first bit $b_{0}$ has been generated and the output has the form $b_{0} \dots$ where the dots represent the remaining $l_{out} - 1$ bits, which are unknown. After the second flip, the output has two bits generated and $l_{out} - 2$ unknown bits - there are $2^{l_{out} - 2}$ remaining possibilities for the final output string. Each coin flip halves the number of possibilities for the output until the final flip settles on a single output. Since a function can only have a single output for a given input, deciding this output is like picking a function from all possible functions. The probability that we get a specific function $H$ is $\frac{1}{2 ^{l_{out}}}$ - the same as if simply choosing a function from a uniform distribution.

Note

A random function is still deterministic in the sense that when input the same data block it will always give the same output.

Unfortunately, truly random functions present a great implementational challenge for classical computers due to their difficulty in obtaining true randomness. A computer cannot really "flip a coin $l_{out}$ times" and is limited by its external randomness sources.

This is why we have to settle for pseudorandom functions.

Definition: Pseudorandom Function (PRF)

A pseudorandom function is an efficient algorithm $PRF (i d b : str [S]) \to str [l_{out}]$ such that for every efficient distinguisher $D (func : function< str [S] \to str [l_{out}] >) \to bit$ it holds that

$Pr [D (PRF) = 1] - H \leftarrow_{R} {0, 1}^{S} \to {0, 1}^{l_{out}} Pr [D (H) = 1] \leq ϵ (S)$

for some negligible $ϵ$ .

Definition Breakdown

The distinguisher $D (func : function< str [S] \to str [l_{out}] >$ takes a function whose inputs are strings of length $S$ and which outputs a string of length $l_{out}$ and tries to determine if the function is a truly random function. This notation means that the distinguisher has oracle access to the function - it can freely query the function with any inputs and can inspect the resulting outputs. Sometimes, the objectively worse notation $D^{f} (1^{S})$ is also used to denote that the distinguisher $D$ has oracle access to the function $f$ .

A function is pseudorandom if there is no efficient distinguisher which can tell the difference between it and a truly random function $H$ which was chosen from the uniform distribution of all functions ${0, 1}^{S} \to {0, 1}^{l_{out}}$ with non-negligible probability.

Pseudorandom functions are useful because they are a generalisation of pseudorandom generators. The length of the output of a PRG must always be greater than the length of its seed, but PRFs allow for an output whose length is independent of the input data block. Mostly, however, they are useful because they produce pseudorandom strings, just like PRGs.

But as with most things in cryptography, it is unknown if pseudorandom functions actually exist. The definition is quite broad in the sense that there should be absolutely no distinguisher which can tell that the function is actually not truly random - a pretty difficult thing to do. So, once again, we are forced to hope that they do exist because otherwise cryptography falls apart - we consider a given algorithm to be a pseudorandom function until someone strings along and proves us wrong. Nevertheless, we still want to make as few assumptions as possible and build the rest on top of it.

Assumption: Existence of a One-Bit Pseudorandom Function

There exists a pseudorandom function $PRF (i d b : str [S]) \to bit$ which outputs a single bit, i.e. $l_{out} = 1$ .

As it turns out, such a pseudorandom function can be used to construct PRFs with any output length. TODO

Pseudorandom Function Generators (PRFGs)

Pseudorandom generators produces pseudorandom strings, while pseudorandom function generators (PRFGs) produce pseudorandom functions.

Definition: Pseudorandom Function Generator (PRFG)

A pseudorandom function generator (PRFG) is an efficient algorithm $PRFG (see d : str [S]) \to function< str [S] \to str [l_{out}] >$ which takes a seed $s \in {0, 1}^{S}$ and outputs a pseudorandom function whose input is a data block of size $S$ and whose output is a string of length $l_{out}$ .

Definition Breakdown

A pseudorandom function generator takes a seed and produces a pseudorandom function. The resulting function takes input data blocks with the same length $S$ as the PRFG's seed and its outputs have length $l_{out}$ . It is common to notate a PRF that was produced by PRFG as $f_{s}$ where $f$ is the function's name and $s$ is the seed used to obtain it.

It is important to remember that the output of a PRFG is a function. Specifically, a PRFG produces a function which takes inputs of the same size as the PRFG's seed. This coincidence has unfortunately led to PRFs and PRFGs commonly being mixed up. It is common to see a PRFG as a two input algorithm $PRFG (see d : str [S], i d b : str [S]) \to str [l_{out}]$ that takes a seed $s$ and an input data block $i$ and acts like a pseudorandom function $PRF_{s} (i)$ . In this case, $PRFG (s, i)$ internally obtains the function $PRF_{s}$ from the seed $s$ and then passes it the data block $i$ . Finally, the PRFG returns the output of the function $PRF_{s}$ .

#![allow(unused)]
fn main() {
fn PRFG(seed: str[S], idb: str[S]) -> str[l_out] {
	let PRF = get_prf_from_seed(seed);
	return PRF(idb);
}
}

PRFGs from PRGs

Okay but how can we construct a PRFG algorithm? Well, as it turns out a pseudorandom generator can be used to construct such algorithms. In particular, a PRG $G (see d : str [S]) \to str [2 S]$ , which takes a seed of length $S$ and outputs a pseudorandom string of double that length, can be used to construct a pseudorandom function generator $PRFG (see d : str [S], i d b : str [S]) \to str [S]$ .

We will denote the first $n$ bits of $G$ 's output as $G_{0} (s)$ and will denote the last $n$ bits of $G$ 's output as $G_{1} (s)$ . For a particular seed $s \in {0, 1}^{S}$ and an input data block $i \in {0, 1}^{S}$ , we define the output of $PRFG (s, i)$ as

$PRFG (s, i) : = G_{i [n - 1]} (G_{i [n - 2]} (\dots G_{i [1]} (G_{i [0]} (s))))$

The PRFG begins by invoking the PRG $G$ on the seed $s$ . If the first bit of $i$ is 0, then we use the first $n$ bits of $G^{'}$ output, i.e. $G_{0} (s)$ , as the seed for the next call to $G$ . Conversely, if the first bit of $i$ is 1, then we use the last $n$ bits of $G$ 's output, i.e. $G_{1} (s)$ , as the seed for the next call to $G$ . In general, at the $j$ -th iteration (counting from 0) we use either the first or the last $n$ bits of the previous iteration's output as the new seed, depending on the bit $i [j - 1]$ .

This can be illustrated by the following tree diagram:

The value $PRFG (s, i)$ is simply the value inside the node $v_{i}$ which is the leaf node at position $i$ when treating the data block string $i$ as a number and counting from left to right. Alternatively, one can think of this as starting at the top and proceeding downwards. At the $j$ -th step we examine the $j$ -th bit of $i$ (i.e. $i [j - 1]$ ) and we either take the left path, if $i [j - 1]$ is 0, or we take the right path, if $i [j - 1]$ is 1. The final node we arrive at will contain the value to be returned for $PRFG (s, i)$ .

The intuition behind why this is indeed a PRFG is pretty simple - if $G$ is a secure pseudorandom generator, the output at each iteration is a pseudorandom string. Therefore, the output at the last iteration must also be a pseudorandom string.

Proof: PRFG from PRG

TODO

The Cyberclopaedia

Pseudorandom Functions

Pseudorandom Function Generators (PRFGs)

PRFGs from PRGs