Paper 2025/1176

A Practical Randomized Nearest-Colattice Framework for Arbitrary Norms

Abstract

The approximate Closest Vector Problem (CVP) is a core computational problem underlying many post-quantum lattice-based signature schemes, including Dilithium, one-more-ISIS, and HuFu. While the security of these schemes is typically expressed in terms of the Inhomogeneous Short Integer Solution (ISIS) problem, it is well-known that ISIS can be efficiently reduced to approximate CVP. Despite its foundational role, approximate CVP with non-negligible approximation factors remains far less explored than other lattice problems such as SVP or LWE, creating a critical gap in both theory and practice. In this work, we bridge this gap by advancing the Colattice framework for solving approximate CVP with large approximation factors. More concretely, (1) We define a practical version of the Colattice algorithm and propose a randomized Nearest Colattice for generating more than one approximate closest vector. (2) Define a formal strategy space for blockwise approximate CVP. (3) Propose a polynomial-time strategy selection algorithm and prove its correctness under standard lattice heuristics. (4) Building on this, we design an efficient security estimator for approximate CVP in both Euclidean and Infinity norms, and extend it to approximate batch-CVP attack settings. (5) By applying this estimator, we perform concrete security evaluations of Dilithium, HuFu, and one-more-ISIS. Our results reveal that almost none of the evaluated schemes withstand approximate batch-CVP attacks with $2^{32}$ queries. (6) We integrate a slicer and Colattice into G6K-CPU, leveraging the Locality-Sensitive Hashing (LSH) technqiue for nearest neighbors search (NNS). This is the first practical implementation of an NNS-accelerated slicer. Our results demonstrate the practical efficiency of approximate CVP and batch-CVP attacks, highlighting the need for more accurate security estimation. These findings underscore the practical importance of accurate approximate CVP modeling and call for a reassessment of current parameter sets in post-quantum signature schemes.

Note: We updated our model to a Randomized Nearest Colattice Algorithm, which can solve the approximate batch-cvp problems.