FHE Lab

Encrypted AI Compute Without Data Exposure

We enable privacy‑preserving analytics and vector search using CKKS‑based homomorphic encryption. The server computes, but never sees your data.

Client Encrypts data

Server Computes on ciphertext

Result Decrypted locally

Privacy by design — no raw data leaves the client.

Why It Matters

AI systems are data‑hungry, but sensitive datasets can’t be shared freely. FHE lets you compute on encrypted data, preserving privacy without giving up utility.

What We Built

CKKS library in Rust with Node/WASM bindings.
Encrypted credit scoring demo (marketplace).
Query‑private encrypted vector search demo.

Use Cases

Risk scoring without exposing customer data.
Private similarity search over sensitive datasets.
Secure analytics for regulated industries.

Marketplace Demo

Users encrypt inputs client‑side. Providers run models on encrypted data and return encrypted results.

Open Demo

Vector Search Demo

Encrypted query, plaintext DB. The server computes similarity scores without seeing the query.

Open Demo

Want a private pilot?

We can deploy privacy‑preserving analytics on your data.

hello@fhelab.net