Concrete ML

Concrete ML is an open-source toolkit built on top of The Concrete Framework. It enables data scientists to automatically turn their machine learning models into their homomorphic equivalent. Read our whitepaper here, and star the project on Github:

FHE is a powerful cryptographic tool that allows cloud service providers to perform computations directly on encrypted data without needing to decrypt first. With FHE, developers can build applications which ensure full privacy of users and are, at the same time, the complete equivalent of their insecure counterparts.

With Concrete ML, we aim to make it as simple as possible for users of machine learning frameworks to understand and use FHE. To that end, we provide APIs which are as close as possible to what data scientists are already using.

Concrete ML Makes Use Cases Easy

Concrete ML can be used with models that are already familiar to data scientists. Logistic regressions, for example, are a popular class of algorithm in Machine Learning. With Concrete ML, you can build a simple logistic regression using scikit-learn to show that they can be executed homomorphically.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from import LogisticRegression
# Create a synthetic dataset
N_TEST = 20
x, y = make_classification(n_samples=N_EXAMPLE_TOTAL, class_sep=2, n_features=4, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    x, y, test_size=N_TEST / N_EXAMPLE_TOTAL, random_state=42)
# Fix the quantization to 3 bits
model = LogisticRegression(n_bits=3)
# Fit the model, y_train)
# We run prediction on non-encrypted data as a reference
y_pred_clear = model.predict(X_test, execute_in_fhe=False)
# We compile into an FHE model
# We then run the inference in FHE
y_pred_fhe = model.predict(X_test, execute_in_fhe=True)
print("In clear  :", y_pred_clear)
print("In FHE    :", y_pred_fhe)
print("Comparison:", (y_pred_fhe == y_pred_clear))

Concrete Numpy

Concrete Numpy underpins Concrete ML. It is a python package that contains the tools data scientists need to compile various numpy functions into their FHE equivalents.


import concrete.numpy as hnp

def add(x, y):
    return x + y

inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4, 5), (5, 4)]
compiler = hnp.NPFHECompiler(add, {"x": "encrypted", "y": "encrypted"})

circuit = compiler.compile_on_inputset(inputset)

examples = [(3, 4), (1, 2), (7, 7), (0, 0)]
for example in examples:
    result =  circuit.encrypt_run_decrypt(*example)
    print(f"Evaluation of {' + '.join(map(str, example))} homomorphically = {result}")

ML toolkit

Still in an early version, it allows for example to turn some torch programs into numpy, and then to use the main API stack to finally get an FHE program.


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Start using Concrete ML

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