Notes from the Deep End

There is one intriguing, annoying and painful fact that not many security experts want to see, let alone accept.

I wanted to understand post-quantum cryptography beyond high-level explanations, so I implemented CRYSTALS-Kyber from scratch in Python.

Earlier we discussed latent spaces (in the context of AE and VAE).

In the previous article we noticed that with autoencoders the learned latent space was very sparse.

The world of deep learning has evolved dramatically over the last decade or two.

[This text was originally published in Viestimies-journal.

This article was originally published in Viestimies-journal in Finnish.

The problem setup I worked in a startup focusing on open-source data-platforms.

T-SNEis well-known dimensionality reduction algorithm particularly suited for the visualization of high-dimensional datasets.

For decades, defense departments were the undisputed champions of technological innovation.

Introduction We have defined the core of CRYSTALS-Kyber algorithm in the previous article.

In this article we will go through how CRYSTALS-Kyber is implemented and refer to a (pedagogical) reference implementation you can find from my github.

Introduction CRYSTALS ("Cryptographic Suite for Algebraic Lattices") is post-quantum public key encryption scheme, meaning it is expected to be secure even at the era of quantum computing where many current PKE-variants fail.

The F-117 Nighthawk stealth fighter re-defined air force capabilities at the time.