Applied FHE: A Use Case Showcase

Identify individuals from encrypted face embeddings without decryption. Biometric data is never exposed as plaintext on the server.

Search an image database with natural language queries. Privacy-preserving with multimodal embeddings and FHE vector search.

Upload an image to find similar images and auto-generated tags using encrypted multimodal vector search.

Find relevant evidence documents instantly from 23,000+ legal records using encrypted vector search. Source documents stay encrypted on the server.

Search and answer questions over DRM-protected corporate documents without ever decrypting them. Enable GenAI adoption without security approval bottlenecks.

Detect similar weak password patterns without sending passwords in plaintext to the server. Encrypted vector comparison — not hashes, not plaintext.

Upload or record audio to detect emergency sounds — sirens, breaking glass, fire, chainsaw — using FHE-encrypted queries over 576 ESC-50 clips. The server never sees your audio in plaintext.

Search thousands of medical images using encrypted query vectors. Diagnostic images and patient data never leave the encrypted domain — privacy-preserving retrieval for radiology and clinical AI.

A case study of privacy-preserving credit decisions at Northline Card Co. — encrypted behavioral features inform credit scoring without exposing raw customer data to the decision engine.

CurtainWall lets enterprise teams query sensitive data lakes without decryption. FHE acts as the outer wall — compute on encrypted data, expose no plaintext, satisfy compliance automatically.

Find similar mechanical parts from an encrypted 3D shape index. Query point clouds are encrypted client-side — the server searches without ever seeing your design geometry.

Standard vector DBs allow embedding inversion — reconstructing original text from embeddings. enVector blocks this attack at the source by encrypting embeddings with FHE.