Computing Individual Discrete Logarithms Faster in GF(p n ) with the NFS-DL Algorithm
نویسنده
چکیده
The Number Field Sieve (NFS) algorithm is the best known method to compute discrete logarithms (DL) in finite fields Fpn , with p medium to large and n ≥ 1 small. This algorithm comprises four steps: polynomial selection, relation collection, linear algebra and finally, individual logarithm computation. The first step outputs two polynomials defining two number fields, and a map from the polynomial ring over the integers modulo each of these polynomials to Fpn . After the relation collection and linear algebra phases, the (virtual) logarithm of a subset of elements in each number field is known. Given the target element in Fpn , the fourth step computes a preimage in one number field. If one can write the target preimage as a product of elements of known (virtual) logarithm, then one can deduce the discrete logarithm of the target. As recently shown by the Logjam attack, this final step can be critical when it can be computed very quickly. But we realized that computing an individual DL is much slower in mediumand large-characteristic non-prime fields Fpn with n ≥ 3, compared to prime fields and quadratic fields Fp2 . We optimize the first part of individual DL: the booting step, by reducing dramatically the size of the preimage norm. Its smoothness probability is higher, hence the running-time of the booting step is much improved. Our method is very efficient for small extension fields with 2 ≤ n ≤ 6 and applies to any n > 1, in medium and large characteristic.
منابع مشابه
Computing Individual Discrete Logarithms Faster in GF(p^n)
The Number Field Sieve (NFS) algorithm is the best known method to compute discrete logarithms (DL) in large characteristic finite fields Fpn , with p large and n ≥ 1 small. This algorithm comprises four steps: polynomial selection, relation collection, linear algebra and finally, individual logarithm computation. The first step outputs two numbers fields equipped with a map to Fpn . After the ...
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