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Quick Start
To compute on encrypted data, you first need to define the function that you want to compute, then compile it into a Concrete-Numpy
Circuit, which you can use to perform homomorphic evaluation.Here is the full example that we will walk through:
import concrete.numpy as cnp
def add(x, y):
return x + y
compiler = cnp.Compiler(add, {"x": "encrypted", "y": "clear"})
inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1)]
circuit = compiler.compile(inputset)
x = 4
y = 4
clear_evaluation = add(x, y)
homomorphic_evaluation = circuit.encrypt_run_decrypt(x, y)
print(x, "+", y, "=", clear_evaluation, "=", homomorphic_evaluation)
Everything you need to perform homomorphic evaluation is included in a single module:
import concrete.numpy as cnp
In this example, we will compile a simple addition function:
def add(x, y):
return x + y
To compile the function, you need to create a
Compiler by specifying the function to compile and encryption status of its inputs:compiler = cnp.Compiler(add, {"x": "encrypted", "y": "clear"})
An inputset is a collection representing the typical inputs to the function. It is used to determine the bit widths and shapes of the variables within the function.
It should be an iterable, yielding tuples of the same length as the number of arguments of the function being compiled:
inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1)]
All inputs in the inputset will be evaluated in the graph, which takes time. If you're experiencing long compilation times, consider providing a smaller inputset.
You can use the
compile method of a Compiler class with an inputset to perform the compilation and get the resulting circuit back:circuit = compiler.compile(inputset)
You can use the
encrypt_run_decrypt method of a Circuit class to perform homomorphic evaluation:homomorphic_evaluation = circuit.encrypt_run_decrypt(4, 4)
circuit.encrypt_run_decrypt(*args) is just a convenient way to do everything at once. It is implemented as circuit.decrypt(circuit.run(circuit.encrypt(*args))).Last modified 9mo ago