Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors

Samuel Stevens, Emily Wenger, Cathy Yuanchen Li, Niklas Nolte, Eshika Saxena, Francois Charton, Kristin E. Lauter

Abstract: Learning with Errors (LWE) is a hard math problem underlying recently standardized post-quantum cryptography (PQC) systems for key exchange and digital signatures. Prior work proposed new machine learning (ML)-based attacks on LWE problems with small, sparse secrets, but these attacks require millions of LWE samples to train on and take days to recover secrets. We propose three key methods---better preprocessing, angular embeddings and model pre-training---to improve these attacks, speeding up preprocessing by $25\times$ and improving model sample efficiency by $10\times$. We demonstrate for the first time that pre-training improves and reduces the cost of ML attacks on LWE. Our architecture improvements enable scaling to larger-dimension LWE problems: this work is the first instance of ML attacks recovering sparse binary secrets in dimension $n=1024$, the smallest dimension used in practice for homomorphic encryption applications of LWE where sparse binary secrets are proposed, albeit for larger modulus $q$. Our ML-based approach is the only attack which has successfully recovered secrets for these parameters.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: * Updated author list.

Assigned Action Editor: ~Georgios_Leontidis1

Submission Number: 3710