Mind the Gap? Not for SVP Hardness under ETH!

We prove new hardness results for fundamental lattice problems under the Exponential Time Hypothesis (ETH). Building on a recent breakthrough by Bitansky et al.\ \cite{BHIRW24}, who gave a polynomial-time reduction from $\mathsf{3SAT}$ to the (gap) $\mathsf{MAXLIN}$ problem-a class of CSPs with linear equations over finite fields-we derive ETH hardness for several lattice problems. First, we show that for any $p \in [1, \infty)$, there exists an explicit constant $\gamma > 1$ such that $\mathsf{CVP}_{p,\gamma}$ (the $\ell_p$-norm approximate Closest Vector Problem) does not admit a $2^{o(n)}$-time algorithm unless ETH is false. Our reduction is deterministic and proceeds via a direct reduction from (gap) $\mathsf{MAXLIN}$ to $\mathsf{CVP}_{p,\gamma}$. Our main contribution is a randomized ETH hardness result for $\mathsf{SVP}_{p,\gamma}$ (the $\ell_p$-norm approximate Shortest Vector Problem) for all $p \in (2, \infty)$. This result relies on a novel geometric property of the integer lattice $\mathbb{Z}^n$ in the $\ell_p$ norm, which says that for any $p \in (2, \infty)$, the number of lattice vectors close to $\frac{1}{2}\vec{1}_n$ (in the $\ell_p$ norm) is exponentially larger than the number of short vectors (namely those close to the origin). We establish this property via a new inequality for the Theta function, which we use to get a randomized reduction from $\mathsf{CVP}_{p,\gamma}$ to $\mathsf{SVP}_{p,\gamma'}$. Finally, we also use our ideas to give some minor improvements over prior reductions from $\mathsf{3SAT}$ to $\mathsf{BDD}_{p,\alpha}$ (the Bounded Distance Decoding Problem), yielding better ETH hardness results for $\mathsf{BDD}_{p,\alpha}$ for any $p \in [1, \infty)$ and $\alpha > \alpha_p^{\ddagger}$, where $\alpha_p^{\ddagger}$ is an explicit threshold depending on $p$.