Enhanced privacy-preserving neural networks with fully homomorphic encryption: Optimized search and training

This research paper describes methods for making neural networks (AI models that learn patterns from data) more private by using fully homomorphic encryption (a type of encryption that lets computers perform calculations on encrypted data without decrypting it first). The work focuses on optimizing how these privacy-protecting neural networks search through and train on data while keeping information secure.