English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Learning with Errors (LWE) is a hard math problem underpinning many proposedpost-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism(KEM) standardized by NIST is based on module~LWE, and current publiclyavailable PQ Homomorphic Encryption (HE) libraries are based on ring LWE. Thesecurity of LWE-based PQ cryptosystems is critical, but certain implementationchoices could weaken them. One such choice is sparse binary secrets, desirablefor PQ HE schemes for efficiency reasons. Prior work, SALSA, demonstrated amachine learning-based attack on LWE with sparse binary secrets in smalldimensions ($n \le 128$) and low Hamming weights ($h \le 4$). However, thisattack assumes access to millions of eavesdropped LWE samples and fails athigher Hamming weights or dimensions.  We present PICANTE, an enhanced machine learning attack on LWE with sparsebinary secrets, which recovers secrets in much larger dimensions (up to$n=350$) and with larger Hamming weights (roughly $n/10$, and up to $h=60$ for$n=350$). We achieve this dramatic improvement via a novel preprocessing step,which allows us to generate training data from a linear number of eavesdroppedLWE samples ($4n$) and changes the distribution of the data to improvetransformer training. We also improve the secret recovery methods of SALSA andintroduce a novel cross-attention recovery mechanism allowing us to read offthe secret directly from the trained models. While PICANTE does not threatenNIST's proposed LWE standards, it demonstrates significant improvement overSALSA and could scale further, highlighting the need for future investigationinto machine learning attacks on LWE with sparse binary secrets.

SALSA PICANTE: a machine learning attack on LWE with binary secrets