Fine-grained deterministic hardness of the shortest vector problem

Let $γ$-$\mathsf{GapSVP}_p$ be the decision version of the shortest vector problem in the $\ell_p$-norm with approximation factor $γ$, let $n$ be the lattice rank and $0<\varepsilon\leq 1$. We prove that there is no algorithm that solves $(2-\varepsilon)$-$\mathsf{GapSVP}_p$ uniformly for all $p\in\mathbb{N}$ in time\[ 2^{2^{o(p)}}\cdot 2^{o(n)},\] unless the Exponential Time Hypothesis is false. The proof is based on a deterministic Karp reduction from a constrained variant of the subset-sum problem to $\mathsf{GapSVP}_p$ for fixed $p$. While most hardness results for the shortest vector problem in finite norms rely on randomized reductions, our method is entirely deterministic. As a consequence, we also obtain a deterministic Karp reduction from the standard subset-sum problem to $(2-\varepsilon)$-$\mathsf{GapSVP}_{\infty}$.