SWIFFT

In cryptography, SWIFFT is a collection of hash functions. It is based on the concept of the (FFT). SWIFFT is not the first hash function based on FFT, but it sets itself apart by providing a mathematical proof of its security. It also uses the . It can be shown that finding collisions in SWIFFT is at least as difficult as finding short vectors in cyclic/ in the worst case. By giving a security reduction to the worst-case scenario of a difficult mathematical problem, SWIFFT gives a much stronger security guarantee than most other .

Unlike many other provably secure hash functions, the algorithm is quite fast, yielding a throughput of 40MB/s on a 3.2 GHz Intel Pentium 4. Although SWIFFT satisfies many desirable cryptographic and statistical properties, it was not designed to be an “all-purpose” cryptographic hash function. For example, it is not a , and would not be a suitable instantiation of a random oracle. The algorithm is less efficient than most traditional hash functions that do not give a proof of their collision-resistance. Therefore, its practical use would lie mostly in applications where the proof of collision-resistance is particularly valuable, such as digital signatures that must remain trustworthy for a long time.

A modification of SWIFFT called was proposed as a candidate for SHA-3 function to the NIST hash function competition and was rejected in the first round.

1 The algorithm
- 1.1 Example
2 Algebraic description
3 Computing the polynomial product
- 3.1 Fast Fourier transform
- 3.2 Number-theoretic transform
4 Parameter choice
5 Statistical properties
6 Cryptographic properties and security
- 6.1 Theoretical security
- 6.2 Practical security
7 Source
8 See Also on BitcoinWiki

The algorithm

The algorithm is as follows:

Let the variable be called $alpha$
Input: message $M$ of length $mn$
Convert $M$ to a collection of $m$ polynomials $p_i$ in a certain $R$ with binary coefficients.
Compute the Fourier coefficients of each $p_i$ using SWIFFT.
Define the Fourier coefficients of $a_i$ , so that they are fixed and depend on a family of SWIFFT.
Point-wise multiply the Fourier coefficients $p_i$ with the Fourier coefficients of $a_i$ for each $i$ .
Use inverse FFT to obtain $m$ polynomials $f_i$ of degree $<2n$ .
Compute $f = sum_{i=1}^m (f_i)$ modulo $p$ and $alpha^n+1$ .
Convert $f$ to $nlog(p)$ bits and output it.

The operation in step 4 is easy to invert, and is performed to achieve , that is, to mix the input bits.
The in step 6 achieves , since it compresses the input.
This is just a high level description of what the algorithm does, some more advanced optimizations are used to finally yield a high performing algorithm.

Example

We choose concrete values for the parameters n, m, and p as follows: n = 64, m= 16, p= 257. For these parameters, any fixed compression function in the family takes a binary input of length mn = 1024 bits (128 bytes), to an output in the range $mathbb{Z}^n_p$ , which has size $p^n = 257^{64}$ . An output in $mathbb{Z}^n_p$ can easily be represented using 528 bits (66 bytes).

Algebraic description

The SWIFFT functions can be described as a simple algebraic expression over some $R$ . A family of these functions depends on three main parameters: let $n$ be a power of 2, let $m > 0$ be a small integer, and let $p > 0$ be a modulus (not necessarily prime, but is convenient to choose it prime). Define $R$ to be the ring $R = mathbb{Z}_p[alpha]/(alpha^n + 1)$ , i.e., the ring of polynomials in $alpha$ having integer coefficients, modulo $p$ and $alpha^n +1$ . An element of $R$ can be written as a polynomial of degree $< n$ having coefficients in $Z_p$ . A certain function in the SWIFFT family is specified by $m$ fixed elements $a_1,ldots,a_m in R$ of the ring $R$ , that are called multipliers. The function corresponds to the following equation over the ring R:

$sum_{i=1}^m (a_i cdot x_i)$

The $x_1,ldots, x_m in R$ are polynomials with binary coefficients, and corresponding to the binary input of length $mn$ .

Computing the polynomial product

To compute the above expression, the main problem is to compute the polynomial products $a_i cdot x_i$ . A fast way to compute these products is given by the . This says that under certain restrictions the following holds:

mathcal{F}{f*g} = mathcal{F}{f} cdot mathcal{F}{g}

Here $mathcal{F}$ denotes the and $cdot$ denotes the pointwise product. In the general case of the convolution theorem $*$ does not denote multiplication but . It can however be shown that polynomial multiplication is a convolution.

Fast Fourier transform

For finding the Fourier transform we will use FFT () which finds the transform in $O(n log(n))$ time. The multiplication algorithm now goes as follows: We use FFT to compute (all at once) the of each polynomial. Then we pointwise multiply the respective Fourier coefficients of the two polynomials, and finally we us an inverse FFT to return a polynomial of degree $< 2n$ .

Number-theoretic transform

Instead of the normal Fourier transform SWIFFT uses the . Number-theoretic transform uses roots of unity in $mathbb{Z}_p$ instead of complex roots of unity. To make this work, we need to ensure that $mathbb{Z}_p$ is a , and that primitive 2n^th roots of unity exist in this field. This can be done by taking $p$ prime such that $2n$ divides $p-1$ .

Parameter choice

The parameters m,p,n are subject to the following restrictions:

n must be a power of 2
p must be prime
p-1 must be a multiple of 2n
$log(p)$ must be greater than m (otherwise the output will not be smaller than the input)

A possible choice is n=64, m=16, p=257. We get a throughput of about 40MB/s, security of about $2^{106}$ operations for finding collisions, and a digest size of 512 bits.

Statistical properties

(Universal hashing). The SWIFFT family of functions is universal. It means that for any fixed distinct $x, x'$ , the probability (over the random choice of $f$ from the family) that $f(x) = f(x')$ is the inverse of the size of the range.
(Regularity). SWIFFT family of compression functions is regular. A function $f$ is said to be regular if, for an input $x$ chosen uniformly at random from the domain, the output $f(x)$ is distributed uniformly over the range.
(Randomness extractor). SWIFFT is a randomness extractor. For hash tables and related applications, it is usually desirable for the outputs of the hash function to be distributed uniformly (or as close to uniformly as possible), even when the inputs are not uniform. Hash functions that give such guarantees are known as randomness extractors, because they distill the non-uniform randomness of the input down to an (almost) uniformly distributed output. Formally, randomness extraction is actually a property of a family of functions, from which one function is chosen at random (and obliviously to the input).

Cryptographic properties and security

SWIFFT is not , due to linearity. For any function $f$ from our family and any two inputs $x_1$ , $x_2$ such that $x_1+x_2$ is also a valid input, we have that $f(x_1)+f(x_2) = f(x_1+x_2)$ . This relation is very unlikely to hold for a random function, so an adversary can easily distinguish our functions from a random function.
It is not claimed by the authors that SWIFFT functions behave like a random oracle. A function is said to behave like a random oracle if it acts like a truly random function. This differs from pseudorandomness in that the function is fixed and public.
SWIFFT family is collision resistant (in an asymptotic sense), under a relatively mild assumption about the difficulty of finding short vectors in cyclic/ideal lattices. This implies that the family is also second preimage resistant.

Theoretical security

SWIFFT is an example of a . As with most security proofs, the security proof of SWIFFT relies on a to a certain difficult to solve mathematical problem. Note that this means that the security of SWIFFT relies strongly on the difficulty of this mathematical problem.

The reduction in the case of SWIFFT is to the problem of finding short vectors in cyclic/. It can be proven that the following holds: Suppose we have an algorithm that for a random version of SWIFFT given by $f$ can find collisions in $f$ within some feasible time $T$ , and with probability $p$ . It is allowed that the algorithm only works in a small but noticeable fraction of the family SWIFFT. Then we can find also an algorithm $f_2$ which can always find a short vector in any ideal lattice over the ring $mathbb{Z}_p[alpha]/(alpha^n + 1)$ in some feasible time $T_2$ , depending on $T$ and $p$ . This means that finding collisions in SWIFFT is at least as difficult as the worst-case scenario of finding short vectors in a lattice over $mathbb{Z}_p[alpha]/(alpha^n + 1)$ . At the moment the fastest algorithms for finding short vectors are all exponential in $n$ . Note that this ensures that there is no significant set of “weak instances” where the security of SWIFFT is weak. This guarantee is not given by most other provably secure hash functions.

Practical security

Known working attacks are the , which takes 2¹⁰⁶ operations and inversion attacks which takes 2⁴⁴⁸ operations for a standard parameter choice. This is usually considered to be enough to render an attack by an adversary infeasible.

Source

http://wikipedia.org/

SWIFFT

SWIFFT

Contents

The algorithm

Example

Algebraic description

Computing the polynomial product

Fast Fourier transform

Number-theoretic transform

Parameter choice

Statistical properties

Cryptographic properties and security

Theoretical security

Practical security

Source

See Also on BitcoinWiki