Crypto Cafe - Zhenisbek Assylbekov - Nov 7, 2023

Title: Intractability of Learning AES with Gradient-based Methods
Abstract: We show the approximate pairwise orthogonality of a class of functions formed by a single AES output bit under the assumption that all of its round keys except the initial one are independent. This result implies the hardness of learning AES encryption (and decryption) with gradient-based methods. The proof relies on the Boas-Bellman type of inequality in inner-product spaces.
Keywords: Advanced Encryption Standard, Block Ciphers, Gradient-based Learning
Bio: Zhenisbek has a PhD in Mathematical Statistics from Hiroshima University. After the PhD and some period of work in industry, he got a job at Nazarbayev University, where he was working as a Teaching Assistant, Instructor, and Assistant Professor in the Department of Mathematics during 2011-2023. Currently, he is an Assistant Professor of Data Science at Purdue University Fort Wayne. His research interests are in machine learning with applications to natural language processing (NLP). He is interested in both the theoretical analysis of machine learning algorithms and the practical implementation and experimental evaluation of such algorithms on text data. He is also interested in hardness of learning which is closely related to cryptography because cryptographic primitives are exactly what is hard for machine learning.