Unformatted text preview: le would pick different primitive polynomials. Since, if p(x) is primitive, then so is xnp(1/x); each entry on the table is actually two primitive polynomials. For example, if (a, b, 0) is primitive, then (a, a - b, 0) is also primitive. If (a, b, c, d, 0) is primitive, then (a, a - d, a - c, a - b, 0) is also primitive. Mathematically: if xa + xb + 1 is primitive, so is xa + xa-b + 1 if xa + xb + xc + xd + 1 is primitive, so is xa + xa-d + xa-c + xa-b + 1 Primitive trinomials are fastest in software, because only two bits of the shift register have to be XORed to generate each new bit. Actually, all the feedback polynomials listed in Table 16.2 are sparse, meaning that they only have a few coefficients. Sparseness is always a source of weakness, sometimes enough to break the algorithm. It is far better to use dense primitive polynomials, those with a lot of coefficients, for cryptographic applications. If you use dense polynomials, and especially if you make them part of the key, you can live with much shorter LFSRs. Generating dense primitive polynomials modulo 2 is not easy. In general, to generate primitive polynomials of degree k you need to know the factorization of 2k - 1. Three good references for finding primitive polynomials are [652,1285,1287]. LFSRs are competent pseudo-random-sequence generators all by themselves, but they have some annoying nonrandom properties. Sequential bits are linear, which makes them useless for encryption. For an LFSR of length n, the internal state is the next n output bits of the generator. Even if the feedback scheme is unknown, it can be determined from only 2n output bits of the generator, by using the highly efficient Berlekamp-Massey algorithm [1082,1083]: see Section 16.3. Also, large random numbers generated from sequential bits of this sequence are highly correlated and, for certain types of applications, not very random at all. Even so, LFSRs are often used as building blocks in encryption algorithms. LFSRs in Software
LFSRs are slow in software, but they’re faster in assembly language than in C. One solution is to run 16 LFSRs (or 32, depending on your...
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This note was uploaded on 10/18/2010 for the course MATH CS 301 taught by Professor Aliulger during the Fall '10 term at Koç University.
- Fall '10