# Linear feedback shift registers

The previous post looked at an algorithm for generating De Bruijn sequences B(k, n) where k is a prime number. These are optimal sequences that contain every possible consecutive sequence of n symbols from an alphabet of size k. As noted near the end of the post, the case k = 2 is especially important in application, i.e. binary sequences.

If we set k = 2, the generating algorithm is an example of a linear feedback shift register (LFSR) sequence.

Here’s the algorithm from the previous post:

1. If (x1, x2, …, xn ) = (0,0, …, 0), return c1.
2. If (x1, x2, …, xn ) = (1,0, …, 0), return 0.
3. Otherwise return c1x1 + c2x2 + … cnxn mod k.

Note that the last line says to return a linear combination of the previous symbols. That is, we operate on the latest n symbols, saving them to registers. We take the feedback from the previous outputs and compute a linear combination. We then shift the register content over by one and add the new output on the end. Hence the name “linear feedback shift register” sequence.

Note that the LFSR algorithm is a linear operator over the field GF(2), except for the special cases in steps 1 and 2. The algorithm couldn’t be entirely linear because it would get stuck; It would produce nothing but zeros forevermore once it encountered an input sequence of all zeros. So technically a LFSR is an “nearly always linear feedback shift register.” It’s linear for 2n − 2 inputs and nonlinear for 2 special inputs.

A LFSR is more general than a binary De Bruijn sequence. For some choices of linear coefficients the output is a De Bruijn sequence, but not for others. If you associate the linear coefficients with polynomial coefficient (with a sign change, as noted in the previous post) then the LFSR sequence is a De Bruijn sequence if and only if the polynomial is a primitive polynomial of degree n.

A few months ago I wrote about LFSRs in the context of random number generation. LFSRs make very efficient random number generators, but they’re not appropriate for cryptography because their linear structure makes them vulnerable to attack. The idea of shrinking generators is use one random number generator to sample another generator. The two generators can both be LFSRs, but the combined generator is nonlinear because the sampling mechanism is nonlinear. The net result is that you can combine two efficient but insecure generators to create a new generator that is efficient and secure.