🤖 AI Summary
This work addresses the challenge of reducing verification errors and improving computational efficiency in AI decision-making while preserving data privacy. The authors propose a Hash-based Homomorphic Artificial Intelligence (HbHAI) framework that, for the first time, integrates repeated error-correcting codes into homomorphic AI systems and introduces a key-dependent hash function. This design enables native AI algorithms to operate directly on encrypted data, allowing verification error to be reduced arbitrarily and achieving up to 10× data compression. Remarkably, under strong privacy guarantees, the approach reduces both computation time and energy consumption by nearly an order of magnitude—outperforming even plaintext processing in certain scenarios.
📝 Abstract
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.