ZATRON Withstands Neural Network Attack on Encrypted Embeddings

ZATRON encrypts document embeddings using a modular barcode scheme that preserves semantic search functionality while protecting against similarity inference attacks. The author tested this encryption by training a neural network on 80,000 labeled similarity pairs—the strongest realistic threat model—and found it achieved only random performance (AUC 0.5), proving genuine security. This demonstrates that meaningful encryption is possible without the computational overhead of traditional homomorphic encryption approaches like FHE or ASPE.