Jaagup Sepp | Searcharxiv

arXiv2026

Arbitrary Reduction of Validation Error for AI Decision Tests using Homomorphic AI and Repetition Codes

This paper presents new results and breakthrough obtained with the HbHAI techniques (Hash-based Homomorphic Artificial Intelligence) proposed in \cite{filiol0,sepp}. HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. It enables to analyse and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented performances compared to existing homomorphic encryption schemes and most notably compared to the same processing on corresponding plaintext data. Two major results have been obtained further. First we enable to reduce the compression rate up to a factor of 10 thus allowing to process massive datasets while reducing the computation time and the energy footprint in the same order. Second, we show how it is possible to arbitrarily reduce the final validation error of AI-based decision tests by using repetition error-correcting codes.

cs.CR↗

arXiv2026

A New Approach in Cryptanalysis Through Combinatorial Equivalence of Cryptosystems

We propose a new approach in cryptanalysis based on an evolution of the concept of \textit{Combinatorial Equivalence}. The aim is to rewrite a cryptosystem under a combinatorially equivalent form in order to make appear new properties that are more strongly discriminating the secret key used during encryption. We successfully applied this approach to the most secure stream ciphers category nowadays. We first define a concept cipher called Cipherbent6 that capture most of the difficulty of stream cipher cryptanalysis. We significantly outperformed all known cryptanalysis. We applied this approach to the Achterbahn cipher and we obtained again far better cryptanalysis results.

cs.CR↗