Learning With Errors
Recover or distinguish a noisy linear system.
BKW · q ≍ n², width ≍ n · unlimited samples
Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples
Results
| # | PUBLICATION / AUTHORS | DATE | TIME BOUND | SPACE BOUND | EVIDENCE |
|---|---|---|---|---|---|
| 1 | An Improved BKW Algorithm for LWE with Applications to Cryptography and LatticesCrypto 2015SourceBibTeX / detailsclassical provable LWE BKW | 29 Jun 2015 | 2(1/(1/2+2 ln 2)+o(1)) n | 2(1/(1/2+2 ln 2)+o(1)) n | PublishedSource checked |
| 2 | On the Asymptotic Complexity of Solving LWEDesigns, Codes and Cryptography 2018SourceBibTeX / detailsclassical provable LWE BKW | 2018 | 2(2/[2(2−1+1/2)]+o(1)) n | 2(2/[2(2−1+1/2)]+o(1)) n | PublishedSource checked |
Comparison definition & assumptions
- Regime
- Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples
- Computation
- Classical
- Rigor
- Provable
- Instances
- Uniform random equations
- Cost scale
- 2^{c n + o(n)}
- Parameter
- n
- Description
- Uniform-secret LWE, prime q=Θ(n²), discrete-Gaussian width s=Θ(n); Unlimited fresh samples
- Comparison rules
- Use q=Θ(n²), Gaussian width s=Θ(n), and exactly the displayed asymptotic sample budget. Constant Gaussian normalizations do not change these exponents. These are interior parameters c_q=2,c_s=1; they are not the Regev q≈n²,width≈n^{3/2}/polylog(n) regime.
- Tags
- BKW, Gaussian noise, sample budget