Extensions

Concrete supports native Python and NumPy operations as much as possible, but not everything is available in Python or NumPy. So, we provide some extensions ourselves to improve your experience.

fhe.univariate(function)

Allows you to wrap any univariate function into a single table lookup:

import numpy as np
from concrete import fhe

def complex_univariate_function(x):

    def per_element(element):
        result = 0
        for i in range(element):
            result += i
        return result

    return np.vectorize(per_element)(x)

@fhe.compiler({"x": "encrypted"})
def f(x):
    return fhe.univariate(complex_univariate_function)(x)

inputset = [np.random.randint(0, 5, size=(3, 2)) for _ in range(10)]
circuit = f.compile(inputset)

sample = np.array([
    [0, 4],
    [2, 1],
    [3, 0],
])
assert np.array_equal(circuit.encrypt_run_decrypt(sample), complex_univariate_function(sample))

The wrapped function:

• shouldn't have any side effects (e.g., no modification of global state)

• should be deterministic (e.g., no random numbers)

• should have the same output shape as its input (i.e., output.shape should be the same with input.shape)

• each output element should correspond to a single input element (e.g., output[0] should only depend on input[0])

If any of these constraints are violated, the outcome is undefined.

fhe.conv(...)

Allows you to perform a convolution operation, with the same semantic of onnx.Conv:

import numpy as np
from concrete import fhe

weight = np.array([[2, 1], [3, 2]]).reshape(1, 1, 2, 2)

@fhe.compiler({"x": "encrypted"})
def f(x):
    return fhe.conv(x, weight, strides=(2, 2), dilations=(1, 1), group=1)

inputset = [np.random.randint(0, 4, size=(1, 1, 4, 4)) for _ in range(10)]
circuit = f.compile(inputset)

sample = np.array(
    [
        [3, 2, 1, 0],
        [3, 2, 1, 0],
        [3, 2, 1, 0],
        [3, 2, 1, 0],
    ]
).reshape(1, 1, 4, 4)
assert np.array_equal(circuit.encrypt_run_decrypt(sample), f(sample))

Only 2D convolutions without padding and with one groups are supported for the time being.

fhe.maxpool(...)

Allows you to perform a maxpool operation, with the same semantic of onnx.MaxPool:

import numpy as np
from concrete import fhe

@fhe.compiler({"x": "encrypted"})
def f(x):
    return fhe.maxpool(x, kernel_shape=(2, 2), strides=(2, 2), dilations=(1, 1))

inputset = [np.random.randint(0, 4, size=(1, 1, 4, 4)) for _ in range(10)]
circuit = f.compile(inputset)

sample = np.array(
    [
        [3, 2, 1, 0],
        [3, 2, 1, 0],
        [3, 2, 1, 0],
        [3, 2, 1, 0],
    ]
).reshape(1, 1, 4, 4)
assert np.array_equal(circuit.encrypt_run_decrypt(sample), f(sample))

Only 2D maxpooling without padding up to 15-bits is supported for the time being.

fhe.array(...)

Allows you to create encrypted arrays:

import numpy as np
from concrete import fhe

@fhe.compiler({"x": "encrypted", "y": "encrypted"})
def f(x, y):
    return fhe.array([x, y])

inputset = [(3, 2), (7, 0), (0, 7), (4, 2)]
circuit = f.compile(inputset)

sample = (3, 4)
assert np.array_equal(circuit.encrypt_run_decrypt(*sample), f(*sample))

Only scalars can be used to create arrays for the time being.

fhe.zero()

Allows you to create encrypted scalar zero:

from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    z = fhe.zero()
    return x + z

inputset = range(10)
circuit = f.compile(inputset)

for x in range(10):
    assert circuit.encrypt_run_decrypt(x) == x

fhe.zeros(shape)

Allows you to create encrypted tensor of zeros:

from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    z = fhe.zeros((2, 3))
    return x + z

inputset = range(10)
circuit = f.compile(inputset)

for x in range(10):
    assert np.array_equal(circuit.encrypt_run_decrypt(x), np.array([[x, x, x], [x, x, x]]))

fhe.one()

Allows you to create encrypted scalar one:

from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    z = fhe.one()
    return x + z

inputset = range(10)
circuit = f.compile(inputset)

for x in range(10):
    assert circuit.encrypt_run_decrypt(x) == x + 1

fhe.ones(shape)

Allows you to create encrypted tensor of ones:

from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    z = fhe.ones((2, 3))
    return x + z

inputset = range(10)
circuit = f.compile(inputset)

for x in range(10):
    assert np.array_equal(circuit.encrypt_run_decrypt(x), np.array([[x, x, x], [x, x, x]]) + 1)