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 compr...
| Authors | Eric Filiol, Jaagup Sepp |
|---|---|
| Primary category | cs.CR |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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