he-man-openfhe

setup

• create a virtual env and activate it
• install OpenFHEPy into the virtual environment
• install project dependencies: `pip install .

or use the docker image

usage

run this command for an interactive help

he-man-openfhe --help

note: all parameters defined in config.py can also be set by setting enviorment variables

MNIST demo

This is a step-by-step manual to demonstrate the interface of he-man-openfhe by classifying MNIST images.
Input sample: demo/mnist/input.npy

Step 0: Model Training (optional)
Train an MNIST classifier by executing scripts/train_mnist_model.py. The resulting model will be saved at demo/mnist/mnist.onnx. This step is optional, as the repository contains a pre-trained model.

Step 1: Keyparams Generation
The model owner calls keyparams together with the model (-m) and a calibration-data container (zip/npz) (-c). The calibration-data is a set of sample inputs that is used to derive meta-data for the subsequent key generation. The keyparams are saved at the defined location (-o). Moreover, a calibrated model is generated and saved with the suffix _calibrated in the filename (i.e. mnist.onnx => mnist_calibrated.onnx). The calibrated model should be used for inference.

he-man-openfhe keyparams -m demo/mnist/mnist.onnx -c demo/mnist/calibration-data.zip -o demo/mnist/keyparams.json

Step 2: Key Generation
The client generates the keys using the previously computed keyparams (-i). The resulting secret key is saved at the defined location (-o) together with the evaluation key whose filename is appended by .pub.

he-man-openfhe keygen -i demo/mnist/keyparams.json -o demo/mnist/key

Step 3: Encryption
The client encrypts input data using the key (-k) and the cleartext input (-i). The encrypted input is saved at the defined location (-o).

he-man-openfhe encrypt -k demo/mnist/key -i demo/mnist/input.npy -o demo/mnist/input.enc

Step 4: Inference
The model owner performs inference using the calibrated model (-m), the public evaluation key (-k) and the encrypted input (-i). The encrypted result is saved at the defined location (-o).

he-man-openfhe inference -m demo/mnist/mnist_calibrated.onnx -k demo/mnist/key.pub -i demo/mnist/input.enc -o demo/mnist/output.enc

Step 5: Decryption
The client decrypts the encrypted result (-i) using the secret key (-k). The cleartext result is saved at the defined location (-o).

he-man-openfhe decrypt -k demo/mnist/key -i demo/mnist/output.enc -o demo/mnist/output.npy

Result:
The result output.npy contains a numpy-array of length ten, where output neuron seven has the hightest output activation:
[-10.61485652 -1.41687503 -0.2596516 3.86985058 -4.38157674
-7.26738544 -8.80905716 12.59595965 -0.35903776 7.6266054 ]

development setup

• create a virtual env
• install project editable: pip install -e ".[dev]"
• install commit hooks: pre-commit install

run all checks

pre-commit run --all-files

run specific checks

pre-commit run --all-files [HOOK_ID]

check .pre-commit-config.yaml for HOOK_ID

build

python -m build

wheels will be in dist folder

test

run pytest using:

pre-commit run pytest

this will generate a test coverage report in htmlcov