Using the basis in [Schnorr 1991] instead of [Schnorr 2021]
[Sch21]
Fast Factoring Integers by SVP Algorithms
Claus Peter Schnorr
https://eprint.iacr.org/2021/232
Note: This follows the version of March 3, not the one of March 1. This version is much easier to implement with existing lattice reduction software, as it only requires solving SVP in dimension 48 for a 400 bits number to be factored (the previous version mentioned dimensions unreachable by currently known techniques).
Credit: There is not much work behind the script itself. Most of the credit for this implementation goes to sage developers and maintainers, and to the FPLLL team (https://github.com/fplll/) for the underlying lattice reduction software.
Command Line Interface
sage fac.sage b n t
where b is the bit-size of the number to be factored, n the number of elements in the factor basis, and t the number of trials. Passing no parameters or invalid integers will results in default values b=400, n=47, t=100 following the claim of Schnorr.
Experimental results (modulo implementation mistakes):
Running b=400, n=47, t=1000, we obtained 0 Factoring Relation found out of 1000 trials. Closer inspection of typical shortest showed that the vector e is typically very sparse, say a single 1 and a single -1.
Running b=10, n=47, t=1000, we obtained 353 Factoring Relation found out of 1000 trials.
Running b=20, n=47, t=1000, we obtained 65 Factoring Relation found out of 1000 trials.
Running b=40, n=47, t=1000, we obtained 0 Factoring Relation found out of 1000 trials.
This suggest that the approach may be sensible, but that not all short vectors give rise to factoring relations, and that obtaining a sufficient success rate requires much larger lattice dimension than claimed in [Sch21].
Personal study (unfortunately, never written down cleanly) of this approach suggested me that this approach requires solving SVP in dimensions beyond reasonable, leading to a factorization algorithm much slower than the state of the art. My impression is that this is the consensus among experts having spent some time on it as well.
I wish to recall that this approach is not particularly new; Ajtai [Ajt98] attributes it to a (lost ?) manuscript of Adleman while Schnorr mentions an article of Brillhard and Morrison [MB75]. Recent theoretical work on the cryptanalysis of module lattices are doing very similar things over number fields [LPSW19]. Interestingly, this construction can serve other purposes than factoring. For example, Ajtai [Ajt98] used it for a NP-hardness proof. Replacing real logarithm by discrete logarithm also lead to interesting ideas; Chor and Rivest use such a variant to solve lattice problem via easy factorization instances [CR88] inside an old-school lattice-based cryptosystem (see also [DP18] for an asymptotic analysis).
[Adl95]
Factoring and Lattice Reduction
L. Adleman
(Manuscript, 1995)
[MB75]
A method of factoring and the factorization of F7
J Brillhart, MA Morrison
Math. Comp, 1975.
[Ajt98]
The shortest vector problem in L2 is NP-hard for randomized reductions
M. Ajtai
https://dl.acm.org/doi/pdf/10.1145/276698.276705
[LPSW19]
An LLL algorithm for module lattices
C Lee, A Pellet-Mary, D Stehlé, A Wallet
https://eprint.iacr.org/2019/1035
[CR88]
A knapsack-type public key cryptosystem based on arithmetic in finite fields
B Chor, RL Rivest
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9452&rep=rep1&type=pdf
[DP19]
Polynomial Time Bounded Distance Decoding near Minkowski’s Bound in Discrete Logarithm Lattices
Léo Ducas and Cécile Pierrot
https://eprint.iacr.org/2018/146