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zeekay
531fc57003
Brand-only sweep. Touches READMEs, docs, package metadata, comments,
notebook markdown, requirements.txt files; does NOT yet touch Python
`concrete.*` imports (those are module-level dependencies requiring the
phase-2 Python module port from `concrete` → `torus`).
Substitutions applied:
Concrete ML → TorusML
concrete-ml → torus-ml
Concrete-ML → Torus-ML
concrete_ml → torus_ml
zama-ai/concrete-ml → luxfhe/ml-sdk
zama-ai/concrete → luxfhe/torus
github.com/zama-ai → github.com/luxfhe
docs.zama.ai/concrete-ml → docs.luxfhe.com/ml-sdk
docs.zama.ai/concrete → docs.luxfhe.com/torus
Zama Concrete ML → Lux TorusML
Zama Concrete → Lux Torus
Zama tfhe-rs / Zama TFHE-rs → Lux-FHE
tfhe-rs / TFHE-rs → Lux-FHE
Zama fhEVM / fhEVM → TorusEVM
Concrete framework → Torus framework
Concrete compiler → Torus compiler
Concrete by Zama → Torus by Lux
the Zama team → the Lux Industries team
Zama's → Lux Industries'
(Zama) → (Lux)
CoFHE / Fhenix → (removed)
LICENSE: original Zama copyright RETAINED per BSD-3-Clause-Clear §1-§2
requirements; Lux Industries modifications copyright added. Per §3 no
name of ZAMA used to endorse the derived work — product marketed
exclusively as TorusML.
README image URLs migrated to github.com/luxfhe/ml-sdk/assets to break
the dependency on Zama's CDN.
Hit count: 350 → 295. Residual hits are Python `from concrete.fhe import`
imports referencing the upstream Zama `concrete` PyPI package; these
require module-level port (phase 2):
Phase 2 plan:
1. Fork zama-ai/concrete (the Python compiler) to luxfhe/torus
2. Rename Python module `concrete` → `torus`; PyPI publish as `torus-fhe`
3. Update ml-sdk imports from `concrete.fhe` → `torus.fhe`, etc.
4. Wire `torus.compiler` GPU dispatch through `luxcpp/gpu` vtbl
(lux-private/gpu-kernels for CUDA/Metal/WebGPU/FPGA targets)
5. Port concrete-cuda + concrete-mlir kernels to
lux-private/gpu-kernels/{cuda,metal,wgsl}/ckks/ (matrix-vector
mul, rescale, rotate, bootstrap for TorusML inference)
Phase 3 plan (kernels):
- CKKS/BGV/BFV GPU kernels in lux-private/gpu-kernels/
(currently only TFHE PBS / keyswitch / blind-rotate exist)
- FPGA bitstreams in lux-private/fpga/ (currently scaffold only)
Phase 1 = docs/branding sweep. PR not opened; user reviews and merges.
Private Authentification in FHE
This folder contains an example that shows how to combine TorusML and Lux-FHE workflows through a privacy-preserving, server-side authentication scenario. In this example, access to a remote server is granted via a token, only if the client's encrypted information meets specific criteria. The returned token thus serves as proof of successful authentication — all without ever exposing any sensitive data.
Overview
To determine whether the client meets the specified requirements, the problem is treated as a classification task. In this example, a decision tree model is used for that purpose and TorusML handles the encrypted inference part, while training is performed on clear data.
The process involves the following steps:
On the developer side:
- Train the decision tree model on clear data
- Compile the model to an FHE circuit using the Lux-FHE ciphertext format
- Deploy the model using TorusML APIs. To know more about it, please refer to the Client-Server Guide and the Client-Server Notebook
On the client Side:
- Encrypt the client's information using a private key and send it to the server.
On the server side:
- Use TorusML to predict whether the client's information is valid. If the prediction is positive, the user is authenticated; otherwise, authentication is denied.
- Use Lux-FHE for the post-processing part to generate a random token (known only to the server) and multiply it by the output of the decision.
- Return the result, the encrypted token if the user is authenticated; otherwise, an encrypted zero vector.
On the client side:
- Decrypt the server's response and send it back to the server to finalize the authentication process.
Installation
- First, create a virtual env and activate it:
python -m venv .venv
source .venv/bin/activate
- Then, install required packages:
pip install -r requirements.txt --ignore-installed