The cool and the cruel: separating hard parts of LWE secrets
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Abstract (arXiv)
Sparse binary LWE secrets are under consideration for standardization for Homomorphic Encryption and its applications to private computation. Known attacks on sparse binary LWE secrets include the sparse dual attack and the hybrid sparse dual-meet in the middle attack which requires significant memory. In this paper, we provide a new statistical attack with low memory requirement. The attack relies on some initial lattice reduction. The key observation is that, after lattice reduction is applied to the rows of a q-ary-like embedded random matrix mathbf A, the entries with high variance are concentrated in the early columns of the extracted matrix. This allows us to separate out the "hard part" of the LWE secret. We can first solve the sub-problem of finding the "cruel" bits of the secret in the early columns, and then find the remaining "cool" bits in linear time. We use statistical techniques to distinguish distributions to identify both the cruel and the cool bits of the secret. We provide concrete attack timings for recovering secrets in dimensions n=256, 512, and 768. For the lattice reduction stage, we leverage recent improvements in lattice reduction (e.g. flatter) applied in parallel. We also apply our new attack in the RLWE setting for 2-power cyclotomic rings, showing that these RLWE instances are much more vulnerable to this attack than LWE.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2024.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025NoMod: A Non-modular Attack on Module Learning With Errorscited by 0
- 2025TAPAS: Datasets for Learning the Learning with Errors Problemcited by 0
- 2026Improving ML Attacks on LWE with Data Repetition and Stepwise Regressioncited by 0
- 2026Discovering Lattice Reduction Strategies via Self-Playcited by 0
- 2026Discovering Lattice Reduction Strategies via Self-Play13 Jun 2026
- 2026Improving ML Attacks on LWE with Data Repetition and Stepwise Regression5 Apr 2026
- 2025TAPAS: Datasets for Learning the Learning with Errors Problem9 Oct 2025
- 2025NoMod: A Non-modular Attack on Module Learning With Errors2 Oct 2025