STEALTH

Secure Transformer for Encrypted Alignment of Latent Text Embeddings.

Model — short description

STEALTH is a 120M-parameter transformer encoder trained to produce encryption-invariant sentence embeddings. It learns a topology-preserving mapping from encrypted text embeddings to a plaintext embedding space using the Semantic Isomorphism Enforcement (SIE) multi-objective loss.

Model specs

• Architecture: 12-layer Transformer encoder with key-attentive attention and multi-key aggregation.
• Model size: ~120M parameters.
• Embedding dim (output): 256.
• Tokenizer: encryption-aware byte-level tokenizer.

Highlights

• ✅ Privacy-first: Operates on ciphertext without requiring decryption.
• ✅ Topology preserving: SIE loss aligns encrypted and plaintext embeddings while preserving semantic distances.
• ✅ Robust training: Multi-key augmentation (multiple ciphertext variants per plaintext) improves invariance and generalization.
• ✅ Practical: Small model footprint (120M) for efficient deployment in constrained environments.