Solving Multivariate Coppersmith Problems with Known Moduli

We examine the problem of finding small solutions to systems of modular multivariate polynomials. While the case of univariate polynomials has been well understood since Coppersmith's original 1996 work, multivariate systems typically rely on carefully crafted shift polynomials and significant manual analysis of the resulting Coppersmith lattice. In this work, we develop several algorithms that make such hand-crafted strategies obsolete. We first use the theory of Gröbner bases to develop an algorithm that provably computes an optimal set of shift polynomials, and we use lattice theory to construct a lattice which provably contains all desired short vectors. While this strategy is usable in practice, the resulting lattice often has large rank. Next, we propose a heuristic strategy based on graph optimization algorithms that quickly identifies low-rank alternatives. Third, we develop a strategy which symbolically precomputes shift polynomials, and we use the theory of polytopes to polynomially bound the running time. Like Meers and Nowakowski's automated method, our precomputation strategy enables heuristically and automatically determining asymptotic bounds. We evaluate our new strategies on over a dozen previously studied Coppersmith problems. In all cases, our unified approach achieves the same recovery bounds in practice as prior work, even improving the practical bounds for three of the problems. In five problems, we find smaller and more efficient lattice constructions, and in three problems, we improve the existing asymptotic bounds. While our strategies are still heuristic, they are simple to describe, implement, and execute, and we hope that they drastically simplify the application of Coppersmith's method to systems of multivariate polynomials.