3 accepted papers
ML attacks on Learning with Errors (LWE) with binary or small secrets only succeed on LWE settings with very simple secrets. For example, they can recover secrets with up to three non-zero bits when models are trained on not-reduced LWE data, and three non-zero bits in the ''cruel region'' [9] when …
Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers. Consequently, "quantum resistant" cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contende…
Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning s…