Measurement example

Use opendp.measurements.make_user_measurement() to construct a measurement for your own mechanism.

Note

This requires a looser trust model, as we cannot verify any privacy or stability properties of user-defined functions.

Python

>>> import opendp.prelude as dp
>>> dp.enable_features("honest-but-curious", "contrib")

This example mocks the typical API of the OpenDP library to make the most private DP mechanism ever!

Python

>>> def make_base_constant(constant):
...     """Constructs a Measurement that only returns a constant value."""
...     def function(_arg: int):
...         return constant
...
...     def privacy_map(d_in: int) -> float:
...         return 0.0
...
...     return dp.m.make_user_measurement(
...         input_domain=dp.atom_domain(T=int),
...         input_metric=dp.absolute_distance(T=int),
...         output_measure=dp.max_divergence(T=float),
...         function=function,
...         privacy_map=privacy_map,
...         TO=type(constant),  # the expected type of the output
...     )

The resulting Measurement may be used interchangeably with those constructed via the library:

Python

>>> meas = (
...     (dp.vector_domain(dp.atom_domain((0, 10))), dp.symmetric_distance())
...     >> dp.t.then_sum()
...     >> make_base_constant("denied")
... )
...
>>> meas([2, 3, 4])
'denied'
>>> meas.map(1) # computes epsilon, because the output measure is max divergence
0.0

While this mechanism clearly has no utility, the code snip may form a basis for you to create own measurements, or even incorporate mechanisms from other libraries.