5 papers
Improving ML Attacks on LWE with Data Repetition and Stepwise Regression
Alberto Alfarano, Eshika Saxena, Emily Wenger +2
The Learning with Errors (LWE) problem is a hard math problem in lattice-based cryptography. In the simplest case of binary secrets, it is the subset sum problem, with error. Effec…
Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation
Bhaktipriya Radharapu, Eshika Saxena, Kenneth Li +3
As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. However, ex…
TAPAS: Datasets for Learning the Learning with Errors Problem
Eshika Saxena, Alberto Alfarano, François Charton +2
AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain paramete…
Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic
Eshika Saxena, Alberto Alfarano, François Charton +3
Recent work showed that ML-based attacks on Learning with Errors (LWE), a hard problem used in post-quantum cryptography, outperform classical algebraic attacks in certain settings…
Benchmarking Attacks on Learning with Errors
Emily Wenger, Eshika Saxena, Mohamed Malhou +2
Lattice cryptography schemes based on the learning with errors (LWE) hardness assumption have been standardized by NIST for use as post-quantum cryptosystems, and by HomomorphicEnc…