Explore >> Select a destination
|
You are here 
|
jeremykun.com | ||
| | | | |
www.ayoub-benaissa.com
|
|
| | | | | This is the first of a series of blog posts about the use of homomorphic encryption for deep learning. Here I introduce the basics and terminology as well as link to external resources that might help with a deeper understanding of the topic. | |
| | | | |
www.jeremykun.com
|
|
| | | | | The Learning With Errors problem is the basis of a few cryptosystems, and a foundation for many fully homomorphic encryption (FHE) schemes. In this article I'll describe a technique used in some of these schemes called modulus switching. In brief, an LWE sample is a vector of values in $\mathbb{Z}/q\mathbb{Z}$ for some $q$, and in LWE cryptosystems an LWE sample can be modified so that it hides a secret message $m$. | |
| | | | |
jeremykun.wordpress.com
|
|
| | | | | The Learning With Errors problem is the basis of a few cryptosystems, and a foundation for many fully homomorphic encryption (FHE) schemes. In this article I'll describe a technique used in some of these schemes called modulus switching. In brief, an LWE sample is a vector of values in $\mathbb{Z}/q\mathbb{Z}$ for some $q$, and in... | |
| | | | |
www.jeremykun.com
|
|
| | | This article was written by my colleague, Cathie Yun. Cathie is an applied cryptographer and security engineer, currently working with me to make fully homomorphic encryption a reality at Google. She's also done a lot of cool stuff with zero knowledge proofs. In previous articles, we've discussed techniques used in Fully Homomorphic Encryption (FHE) schemes. The basis for many FHE schemes, as well as other privacy-preserving protocols, is the Learning With Errors (LWE) problem. | ||
