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awwalker.com | ||
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modexp.wordpress.com
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| | | | | Introduction Compressed, encrypted, and random data all contain a high amount of entropy, which is why many products use entropy analysis to detect malicious code in binaries that have never been examined before. In a previous post about masking, I suggested using a deterministic random number generator with the Fisher-Yates shuffle to try and scramble... | |
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www.jeremykun.com
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| | | | | Last time we covered an operation in the LWE encryption scheme called modulus switching, which allows one to switch from one modulus to another, at the cost of introducing a small amount of extra noise, roughly $\sqrt{n}$, where $n$ is the dimension of the LWE ciphertext. This time we'll cover a more sophisticated operation called key switching, which allows one to switch an LWE ciphertext from being encrypted under one secret key to another, without ever knowing either secret key. | |
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www.ayoub-benaissa.com
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| | | | | 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. | |
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www.jeremykun.com
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| | | 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 $m$. | ||
