Welcome to the resource topic for 2023/1508
Title:
Provable Dual Attacks on Learning with Errors
Authors: Amaury Pouly, Yixin Shen
Abstract:Learning with Errors (LWE) is an important problem for post-quantum cryptography (PQC) that underlines the security of several NIST PQC selected algorithms. Several recent papers have claimed improvements on the complexity of so-called dual attacks on LWE. These improvements make dual attacks comparable to or even better than primal attacks in certain parameter regimes. Unfortunately, those improvements rely on a number of untested and hard-to-test statistical assumptions. Furthermore, a recent paper claims that the whole premise of those improvements might be incorrect.
The goal of this paper is to improve the situation by proving the correctness of a dual attack without relying on any statistical assumption. Although our attack is greatly simplified compared to the recent ones, it shares all the important statistical elements with those attacks and can serve as a basis for the analysis of more advanced attacks.
Our main contribution is to clearly identify a set of parameters under which our attack (and presumably other recent dual attacks) can work. Furthermore, our analysis completely departs from the existing statistics-based analysis and is instead rooted in geometry. We also compare the regime in which our algorithm works to the ``contradictory regime’’ of Ducas and Pulles. We observe that those two regimes are essentially complementary but also that their statistical model does not seem to match what happens in our attack.
ePrint: https://eprint.iacr.org/2023/1508
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