Learning With Errors
Recover or distinguish a noisy linear system.
Primal embedding · Gaussian error
Search-LWE through BDD-to-uSVP embedding with a specified empirical gap-to-reduction model
Results
| # | PUBLICATION / AUTHORS | DATE | TIME BOUND | SPACE BOUND | EVIDENCE |
|---|---|---|---|---|---|
| — | On the concrete hardness of Learning with ErrorsJournal of Mathematical Cryptology 2015SourceBibTeX / detailsprimal embedding uSVP historical cost model | 2 Oct 2015 | TBKZ(m+1,δ₀); log₂δ₀=[log₂(ε′τ α√(2e))]²/(4nlog₂q) | Depends on the SVP oracle and embedding implementation. | PublishedSource checked |
Comparison definition & assumptions
- Regime
- Search-LWE through BDD-to-uSVP embedding with a specified empirical gap-to-reduction model
- Computation
- Classical
- Rigor
- Heuristic / model-dependent
- Instances
- Uniform random LWE samples; explicit parameter contract
- Cost scale
- Parameter-dependent time and space expressions
- Parameter
- n, q, error width, secret distribution, sample budget and attack parameters
- Description
- Historical LWE bounds and cost models with parameters retained. Entries without a common fixed asymptotic scale are listed without a numerical rank.
- Comparison rules
- Specify q(n), error distribution and width convention, secret distribution, available samples, search versus decision objective, success criterion and lattice/algebraic assumptions. Formula-only entries have null coefficients. A concrete instance estimate is not an exponent c in 2^{cn}.
- Tags
- primal, uSVP, embedding, Gaussian error