Linear feedback shift register (LFSR) sequence commands

Stream ciphers have been used for a long time as a source of pseudo-random number generators.

S. Golomb [Go1967] gives a list of three statistical properties that a sequence of numbers \({\bf a}=\{a_n\}_{n=1}^\infty\), \(a_n\in \{0,1\}\) should display to be considered “random”. Define the autocorrelation of \({\bf a}\) to be

\[C(k)=C(k,{\bf a})=\lim_{N\rightarrow \infty} {1\over N}\sum_{n=1}^N (-1)^{a_n+a_{n+k}}.\]

In the case where \({\bf a}\) is periodic with period \(P\), then this reduces to

\[C(k)={1\over P}\sum_{n=1}^P (-1)^{a_n+a_{n+k}}.\]

Assume \({\bf a}\) is periodic with period \(P\).

  • balance: \(|\sum_{n=1}^P(-1)^{a_n}|\leq 1\).

  • low autocorrelation:

    \[\begin{split}C(k)= \left\{ \begin{array}{cc} 1,& k=0,\\ \epsilon, & k\not= 0. \end{array} \right.\end{split}\]

    (For sequences satisfying these first two properties, it is known that \(\epsilon=-1/P\) must hold.)

  • proportional runs property: In each period, half the runs have length \(1\), one-fourth have length \(2\), etc. Moreover, there are as many runs of \(1\)‘s as there are of \(0\)‘s.

A general feedback shift register is a map \(f:{\bf F}_q^d\rightarrow {\bf F}_q^d\) of the form

\[\begin{split}\begin{array}{c} f(x_0,...,x_{n-1})=(x_1,x_2,...,x_n),\\ x_n=C(x_0,...,x_{n-1}), \end{array}\end{split}\]

where \(C:{\bf F}_q^d\rightarrow {\bf F}_q\) is a given function. When \(C\) is of the form

\[C(x_0,...,x_{n-1})=a_0x_0+...+a_{n-1}x_{n-1},\]

for some given constants \(a_i\in {\bf F}_q\), the map is called a linear feedback shift register (LFSR).

Example of an LFSR: Let

\[f(x)=a_{{0}}+a_{{1}}x+...+a_{{n}}{x}^n+...,\]
\[g(x)=b_{{0}}+b_{{1}}x+...+b_{{n}}{x}^n+...,\]

be given polynomials in \({\bf F}_2[x]\) and let

\[h(x)={f(x)\over g(x)}=c_0+c_1x+...+c_nx^n+... \ .\]

We can compute a recursion formula which allows us to rapidly compute the coefficients of \(h(x)\) (take \(f(x)=1\)):

\[c_{n}=\sum_{i=1}^n {{-b_i\over b_0}c_{n-i}}.\]

The coefficients of \(h(x)\) can, under certain conditions on \(f(x)\) and \(g(x)\), be considered “random” from certain statistical points of view.

Example: For instance, if

\[f(x)=1,\ \ \ \ g(x)=x^4+x+1,\]

then

\[h(x)=1+x+x^2+x^3+x^5+x^7+x^8+...\ .\]

The coefficients of \(h\) are

\[\begin{split}\begin{array}{c} 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, \\ 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, ...\ . \end{array}\end{split}\]

The sequence of \(0,1\)‘s is periodic with period \(P=2^4-1=15\) and satisfies Golomb’s three randomness conditions. However, this sequence of period 15 can be “cracked” (i.e., a procedure to reproduce \(g(x)\)) by knowing only 8 terms! This is the function of the Berlekamp-Massey algorithm [Mas1969], implemented in berlekamp_massey.py.

AUTHORS:

  • David Joyner (2005-11-24): initial creation.
  • Timothy Brock (2005-11): added lfsr_sequence with code modified from Python Cookbook, http://aspn.activestate.com/ASPN/Python/Cookbook/
  • Timothy Brock (2006-04-17): added lfsr_autocorrelation and lfsr_connection_polynomial.
sage.crypto.lfsr.lfsr_autocorrelation(L, p, k)

INPUT:

  • L – a periodic sequence of elements of ZZ or GF(2); must have length \(p\)
  • p – the period of \(L\)
  • k – an integer between \(0\) and \(p\)

OUTPUT: autocorrelation sequence of \(L\)

EXAMPLES:

sage: F = GF(2)
sage: o = F(0)
sage: l = F(1)
sage: key = [l,o,o,l]; fill = [l,l,o,l]; n = 20
sage: s = lfsr_sequence(key,fill,n)
sage: lfsr_autocorrelation(s,15,7)
4/15
sage: lfsr_autocorrelation(s,int(15),7)
4/15
sage.crypto.lfsr.lfsr_connection_polynomial(s)

INPUT:

  • s – a sequence of elements of a finite field of even length

OUTPUT:

  • C(x) – the connection polynomial of the minimal LFSR.

This implements the algorithm in section 3 of J. L. Massey’s article [Mas1969].

EXAMPLES:

sage: F = GF(2)
sage: F
Finite Field of size 2
sage: o = F(0); l = F(1)
sage: key = [l,o,o,l]; fill = [l,l,o,l]; n = 20
sage: s = lfsr_sequence(key,fill,n); s
[1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0]
sage: lfsr_connection_polynomial(s)
x^4 + x + 1
sage: from sage.matrix.berlekamp_massey import berlekamp_massey
sage: berlekamp_massey(s)
x^4 + x^3 + 1

Notice that berlekamp_massey returns the reverse of the connection polynomial (and is potentially must faster than this implementation).

sage.crypto.lfsr.lfsr_sequence(key, fill, n)

This function creates an LFSR sequence.

INPUT:

  • key – a list of finite field elements, [c_0,c_1,…,c_k]
  • fill – the list of the initial terms of the LFSR sequence, [x_0,x_1,…,x_k]
  • n – number of terms of the sequence that the function returns

OUTPUT: The LFSR sequence defined by \(x_{n+1} = c_kx_n+...+c_0x_{n-k}\) for \(n \leq k\).

EXAMPLES:

sage: F = GF(2); l = F(1); o = F(0)
sage: F = GF(2); S = LaurentSeriesRing(F,'x'); x = S.gen()
sage: fill = [l,l,o,l]; key = [1,o,o,l]; n = 20
sage: L = lfsr_sequence(key,fill,20); L
[1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0]
sage: from sage.matrix.berlekamp_massey import berlekamp_massey
sage: g = berlekamp_massey(L); g
x^4 + x^3 + 1
sage: (1)/(g.reverse()+O(x^20))
1 + x + x^2 + x^3 + x^5 + x^7 + x^8 + x^11 + x^15 + x^16 + x^17 + x^18 + O(x^20)
sage: (1+x^2)/(g.reverse()+O(x^20))
1 + x + x^4 + x^8 + x^9 + x^10 + x^11 + x^13 + x^15 + x^16 + x^19 + O(x^20)
sage: (1+x^2+x^3)/(g.reverse()+O(x^20))
1 + x + x^3 + x^5 + x^6 + x^9 + x^13 + x^14 + x^15 + x^16 + x^18 + O(x^20)
sage: fill = [l,l,o,l]; key = [l,o,o,o]; n = 20
sage: L = lfsr_sequence(key,fill,20); L
[1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1]
sage: g = berlekamp_massey(L); g
x^4 + 1
sage: (1+x)/(g.reverse()+O(x^20))
1 + x + x^4 + x^5 + x^8 + x^9 + x^12 + x^13 + x^16 + x^17 + O(x^20)
sage: (1+x+x^3)/(g.reverse()+O(x^20))
1 + x + x^3 + x^4 + x^5 + x^7 + x^8 + x^9 + x^11 + x^12 + x^13 + x^15 + x^16 + x^17 + x^19 + O(x^20)