Secure Transformer for Encrypted Alignment of Latent Text Embeddings.
PyTorch
Transformers
Hugging%20Face
License: MIT
Model size
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.
